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Mutono N, Basáñez MG, James A, Stolk WA, Makori A, Kimani TN, Hollingsworth TD, Vasconcelos A, Dixon MA, de Vlas SJ, Thumbi SM. Elimination of transmission of onchocerciasis (river blindness) with long-term ivermectin mass drug administration with or without vector control in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health 2024; 12:e771-e782. [PMID: 38484745 PMCID: PMC11009120 DOI: 10.1016/s2214-109x(24)00043-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 01/11/2024] [Accepted: 01/19/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND WHO has proposed elimination of transmission of onchocerciasis (river blindness) by 2030. More than 99% of cases of onchocerciasis are in sub-Saharan Africa. Vector control and mass drug administration of ivermectin have been the main interventions for many years, with varying success. We aimed to identify factors associated with elimination of onchocerciasis transmission in sub-Saharan Africa. METHODS For this systematic review and meta-analysis we searched for published articles reporting epidemiological or entomological assessments of onchocerciasis transmission status in sub-Saharan Africa, with or without vector control. We searched MEDLINE, PubMed, Web of Science, Embase, Cochrane Central Register of Controlled Trials, African Index Medicus, and Google Scholar databases for all articles published from database inception to Aug 19, 2023, without language restrictions. The search terms used were "onchocerciasis" AND "ivermectin" AND "mass drug administration". The three inclusion criteria were (1) focus or foci located in Africa, (2) reporting of elimination of transmission or at least 10 years of ivermectin mass drug administration in the focus or foci, and (3) inclusion of at least one of the following assessments: microfilarial prevalence, nodule prevalence, Ov16 antibody seroprevalence, and blackfly infectivity prevalence. Epidemiological modelling studies and reviews were excluded. Four reviewers (NM, AJ, AM, and TNK) extracted data in duplicate from the full-text articles using a data extraction tool developed in Excel with columns recording the data of interest to be extracted, and a column where important comments for each study could be highlighted. We did not request any individual-level data from authors. Foci were classified as achieving elimination of transmission, being close to elimination of transmission, or with ongoing transmission. We used mixed-effects meta-regression models to identify factors associated with transmission status. This study is registered in PROSPERO, CRD42022338986. FINDINGS Of 1525 articles screened after the removal of duplicates, 75 provided 282 records from 238 distinct foci in 19 (70%) of the 27 onchocerciasis-endemic countries in sub-Saharan Africa. Elimination of transmission was reported in 24 (9%) records, being close to elimination of transmission in 86 (30%) records, and ongoing transmission in 172 (61%) records. I2 was 83·3% (95% CI 79·7 to 86·3). Records reporting 10 or more years of continuous mass drug administration with 80% or more therapeutic coverage of the eligible population yielded significantly higher odds of achieving elimination of transmission (log-odds 8·5 [95% CI 3·5 to 13·5]) or elimination and being close to elimination of transmission (42·4 [18·7 to 66·1]) than those with no years achieving 80% coverage or more. Reporting 15-19 years of ivermectin mass drug administration (22·7 [17·2 to 28·2]) and biannual treatment (43·3 [27·2 to 59·3]) were positively associated with elimination and being close to elimination of transmission compared with less than 15 years and no biannual mass drug administration, respectively. Having had vector control without vector elimination (-42·8 [-59·1 to -26·5]) and baseline holoendemicity (-41·97 [-60·6 to -23·2]) were associated with increased risk of ongoing transmission compared with no vector control and hypoendemicity, respectively. Blackfly disappearance due to vector control or environmental change contributed to elimination of transmission. INTERPRETATION Mass drug administration duration, frequency, and coverage; baseline endemicity; and vector elimination or disappearance are important determinants of elimination of onchocerciasis transmission in sub-Saharan Africa. Our findings underscore the importance of improving and sustaining high therapeutic coverage and increasing treatment frequency if countries are to achieve elimination of onchocerciasis transmission. FUNDING The Bill & Melinda Gates Foundation and Neglected Tropical Diseases Modelling Consortium, UK Medical Research Council, and Global Health EDCTP3 Joint Undertaking. TRANSLATIONS For the Swahili, French, Spanish and Portuguese translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Nyamai Mutono
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya; Paul G Allen School for Global Health, Washington State University, Pullman, WA, USA.
| | - Maria-Gloria Basáñez
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK; London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Ananthu James
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Anita Makori
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya; Paul G Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Teresia Njoki Kimani
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya; Paul G Allen School for Global Health, Washington State University, Pullman, WA, USA; Ministry of Health Kenya, Kiambu Town, Kenya
| | | | | | - Matthew A Dixon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK; London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - S M Thumbi
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya; Paul G Allen School for Global Health, Washington State University, Pullman, WA, USA; Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, UK
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Vasconcelos A, King JD, Nunes-Alves C, Anderson R, Argaw D, Basáñez MG, Bilal S, Blok DJ, Blumberg S, Borlase A, Brady OJ, Browning R, Chitnis N, Coffeng LE, Crowley EH, Cucunubá ZM, Cummings DAT, Davis CN, Davis EL, Dixon M, Dobson A, Dyson L, French M, Fronterre C, Giorgi E, Huang CI, Jain S, James A, Kim SH, Kura K, Lucianez A, Marks M, Mbabazi PS, Medley GF, Michael E, Montresor A, Mutono N, Mwangi TS, Rock KS, Saboyá-Díaz MI, Sasanami M, Schwehm M, Spencer SEF, Srivathsan A, Stawski RS, Stolk WA, Sutherland SA, Tchuenté LAT, de Vlas SJ, Walker M, Brooker SJ, Hollingsworth TD, Solomon AW, Fall IS. Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions? Clin Infect Dis 2024; 78:S83-S92. [PMID: 38662692 PMCID: PMC11045030 DOI: 10.1093/cid/ciae082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.
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Affiliation(s)
- Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Jonathan D King
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Cláudio Nunes-Alves
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Roy Anderson
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Daniel Argaw
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Shakir Bilal
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - David J Blok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Anna Borlase
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Raiha Browning
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emily H Crowley
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Zulma M Cucunubá
- Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Pontificia Javeriana, Bogotá, Colombia
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - Christopher Neil Davis
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Emma Louise Davis
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Matthew Dixon
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Andrew Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Louise Dyson
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Michael French
- Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, London, United Kingdom
- RTI International, Washington, D.C., USA
| | - Claudio Fronterre
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Saurabh Jain
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Ananthu James
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sung Hye Kim
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Klodeta Kura
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Ana Lucianez
- Communicable Diseases Prevention, Control, and Elimination, Pan American Health Organization, Washington D.C., USA
| | - Michael Marks
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Pamela Sabina Mbabazi
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Graham F Medley
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Edwin Michael
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Antonio Montresor
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Nyamai Mutono
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA
| | - Thumbi S Mwangi
- Centre for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Kat S Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Martha-Idalí Saboyá-Díaz
- Communicable Diseases Prevention, Control, and Elimination, Pan American Health Organization, Washington D.C., USA
| | - Misaki Sasanami
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Markus Schwehm
- ExploSYS GmbH, Interdisciplinary Institute for Exploratory Systems, Leinfelden-Echterdingen, Germany
| | - Simon E F Spencer
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Ariktha Srivathsan
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Robert S Stawski
- Institute of Public Health and Wellbeing, School of Health and Social Care, University of Essex, Essex, United Kingdom
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Samuel A Sutherland
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Warwick Medical School, The University of Warwick, Coventry, United Kingdom
| | | | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, United Kingdom
| | | | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Anthony W Solomon
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - Ibrahima Socé Fall
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
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Srivathsan A, Abdou A, Al-Khatib T, Apadinuwe SC, Badiane MD, Bucumi V, Chisenga T, Kabona G, Kabore M, Kanyi SK, Bella L, M’po N, Masika M, Minnih A, Sitoe HM, Mishra S, Olobio N, Omar FJ, Phiri I, Sanha S, Seife F, Sharma S, Tekeraoi R, Traore L, Watitu T, Bol YY, Borlase A, Deiner MS, Renneker KK, Hooper PJ, Emerson PM, Vasconcelos A, Arnold BF, Porco TC, Hollingsworth TD, Lietman TM, Blumberg S. District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach. Clin Infect Dis 2024; 78:S101-S107. [PMID: 38662700 PMCID: PMC11045026 DOI: 10.1093/cid/ciae031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Assessing the feasibility of 2030 as a target date for global elimination of trachoma, and identification of districts that may require enhanced treatment to meet World Health Organization (WHO) elimination criteria by this date are key challenges in operational planning for trachoma programmes. Here we address these challenges by prospectively evaluating forecasting models of trachomatous inflammation-follicular (TF) prevalence, leveraging ensemble-based approaches. Seven candidate probabilistic models were developed to forecast district-wise TF prevalence in 11 760 districts, trained using district-level data on the population prevalence of TF in children aged 1-9 years from 2004 to 2022. Geographical location, history of mass drug administration treatment, and previously measured prevalence data were included in these models as key predictors. The best-performing models were included in an ensemble, using weights derived from their relative likelihood scores. To incorporate the inherent stochasticity of disease transmission and challenges of population-level surveillance, we forecasted probability distributions for the TF prevalence in each geographic district, rather than predicting a single value. Based on our probabilistic forecasts, 1.46% (95% confidence interval [CI]: 1.43-1.48%) of all districts in trachoma-endemic countries, equivalent to 172 districts, will exceed the 5% TF control threshold in 2030 with the current interventions. Global elimination of trachoma as a public health problem by 2030 may require enhanced intervention and/or surveillance of high-risk districts.
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Affiliation(s)
- Ariktha Srivathsan
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Amza Abdou
- Programme National de Santé Oculaire, Ministère De La Santé Publique, Niamey, Niger
| | - Tawfik Al-Khatib
- Prevention of Blindness Program, Ministry of Public Health & Population, Sana'a, Yemen
| | | | - Mouctar D Badiane
- Programme National de Promotion de La Santé Oculaire, Ministère de la Santé et de L'Action sociale, Dakar, Sénégal
| | - Victor Bucumi
- Département En Charge des Maladies Tropicales Négligées, Ministère De La Santé Publique Et De La Lutte Contre Le Sida, Bujumbura, Burundi
| | - Tina Chisenga
- Ministry of Health Public Health Department, Lusaka, Zambia
| | - George Kabona
- Neglected Tropical Disease Control Program, Ministry of Health and Social Welfare, Dar Es Salaam, United Republic of Tanzania
| | - Martin Kabore
- Programme national de lutte contre les maladies tropicales négligées, Ministère de la santé et de l'hygiène publique, Ouagadougou, Burkina Faso
| | - Sarjo Kebba Kanyi
- The National Eye Health Programme, Ministry of Health and Social Welfare, Banjul, Kanifing, The Gambia
| | - Lucienne Bella
- Programme National De Lutte Contre La Cécité, Ministère De La Santé Publique, Yaoundé, Cameroon
| | - Nekoua M’po
- Programme National De Lutte Contre Les Maladies Transmissibles, Ministère De La Santé, Cotonou, Benin
| | - Michael Masika
- Department of Clinical Services, Ministry of Health, Lilongwe, Malawi
| | - Abdellahi Minnih
- Département Des Maladies Transmissibles, Ministère De La Santé Nouakchott, Nouakchott, Mauritania
| | - Henis Mior Sitoe
- Direcção Nacional De Saúde Pública Ministerio Da Saude, Maputo, Mozambique
| | | | - Nicholas Olobio
- National Trachoma Elimination Programme, Federal Ministry of Health, Abuja, Nigeria
| | | | - Isaac Phiri
- Department of Epidemiology and Disease Control, Ministry of Health & Child Welfare, Harare, Zimbabwe
| | - Salimato Sanha
- Programa Nacional De Saúde De Visão, Minsap, Bissau, Guinea-Bissau
| | - Fikre Seife
- Federal Ministry of Health, Addis Ababa, Ethiopia
| | | | - Rabebe Tekeraoi
- Eye Department, Ministry of Health and Medical Services, South Tarawa, Kiribati
| | - Lamine Traore
- Programme National de la Santé Oculaire, Ministère de la Santé, Bamako, Mali
| | | | - Yak Yak Bol
- Neglected Tropical Diseases Programme, Ministry of Health, Juba, South Sudan
| | - Anna Borlase
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Michael S Deiner
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Kristen K Renneker
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - P J Hooper
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Paul M Emerson
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Benjamin F Arnold
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Travis C Porco
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Thomas M Lietman
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
- Department of Medicine, University of California, San Francisco, California, USA
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Touloupou P, Fronterre C, Cano J, Prada JM, Smith M, Kontoroupis P, Brown P, Rivera RC, de Vlas SJ, Gunawardena S, Irvine MA, Njenga SM, Reimer L, Seife F, Sharma S, Michael E, Stolk WA, Pulan R, Spencer SEF, Hollingsworth TD. An Ensemble Framework for Projecting the Impact of Lymphatic Filariasis Interventions Across Sub-Saharan Africa at a Fine Spatial Scale. Clin Infect Dis 2024; 78:S108-S116. [PMID: 38662704 PMCID: PMC11045016 DOI: 10.1093/cid/ciae071] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Lymphatic filariasis (LF) is a neglected tropical disease targeted for elimination as a public health problem by 2030. Although mass treatments have led to huge reductions in LF prevalence, some countries or regions may find it difficult to achieve elimination by 2030 owing to various factors, including local differences in transmission. Subnational projections of intervention impact are a useful tool in understanding these dynamics, but correctly characterizing their uncertainty is challenging. METHODS We developed a computationally feasible framework for providing subnational projections for LF across 44 sub-Saharan African countries using ensemble models, guided by historical control data, to allow assessment of the role of subnational heterogeneities in global goal achievement. Projected scenarios include ongoing annual treatment from 2018 to 2030, enhanced coverage, and biannual treatment. RESULTS Our projections suggest that progress is likely to continue well. However, highly endemic locations currently deploying strategies with the lower World Health Organization recommended coverage (65%) and frequency (annual) are expected to have slow decreases in prevalence. Increasing intervention frequency or coverage can accelerate progress by up to 5 or 6 years, respectively. CONCLUSIONS While projections based on baseline data have limitations, our methodological advancements provide assessments of potential bottlenecks for the global goals for LF arising from subnational heterogeneities. In particular, areas with high baseline prevalence may face challenges in achieving the 2030 goals, extending the "tail" of interventions. Enhancing intervention frequency and/or coverage will accelerate progress. Our approach facilitates preimplementation assessments of the impact of local interventions and is applicable to other regions and neglected tropical diseases.
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Affiliation(s)
| | | | - Jorge Cano
- Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN), WHO Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
| | - Joaquin M Prada
- School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Morgan Smith
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | | | - Paul Brown
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Rocio Caja Rivera
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, USA
| | - Sake J de Vlas
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Michael A Irvine
- Data and Analytic Services, British Columbia Centre for Disease Control, Vancouver, Canada
| | - Sammy M Njenga
- Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Lisa Reimer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Fikre Seife
- Disease Prevention and Control Directorate, Federal Ministry of Health, Addis Ababa, Ethiopia
| | - Swarnali Sharma
- Department of Mathematics, Vijaygarh Jyotish Ray College, Kolkata, India
| | - Edwin Michael
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, USA
| | - Wilma A Stolk
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rachel Pulan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Simon E F Spencer
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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Vasconcelos A, Nunes-Alves C, Hollingsworth TD. New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases. Clin Infect Dis 2024; 78:S77-S82. [PMID: 38662694 PMCID: PMC11045012 DOI: 10.1093/cid/ciae070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
The World Health Organization roadmap for neglected tropical diseases (NTDs) sets out ambitious targets for disease control and elimination by 2030, including 90% fewer people requiring interventions against NTDs and the elimination of at least 1 NTD in 100 countries. Mathematical models are an important tool for understanding NTD dynamics, optimizing interventions, assessing the efficacy of new tools, and estimating the economic costs associated with control programs. As NTD control shifts to increased country ownership and programs progress toward disease elimination, tailored models that better incorporate local context and can help to address questions that are important for decision-making at the national level are gaining importance. In this introduction to the supplement, New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases, we discuss current challenges in generating more locally relevant models and summarize how the articles in this supplement present novel ways in which NTD modeling can help to accelerate achievement and sustainability of the 2030 targets.
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Affiliation(s)
- Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Cláudio Nunes-Alves
- Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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Prada JM, Touloupou P, Kebede B, Giorgi E, Sime H, Smith M, Kontoroupis P, Brown P, Cano J, Farkas H, Irvine M, Reimer L, Caja Rivera R, de Vlas SJ, Michael E, Stolk WA, Pulan R, Spencer SEF, Hollingsworth TD, Seife F. Subnational Projections of Lymphatic Filariasis Elimination Targets in Ethiopia to Support National Level Policy. Clin Infect Dis 2024; 78:S117-S125. [PMID: 38662702 PMCID: PMC11045027 DOI: 10.1093/cid/ciae072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Lymphatic filariasis (LF) is a debilitating, poverty-promoting, neglected tropical disease (NTD) targeted for worldwide elimination as a public health problem (EPHP) by 2030. Evaluating progress towards this target for national programmes is challenging, due to differences in disease transmission and interventions at the subnational level. Mathematical models can help address these challenges by capturing spatial heterogeneities and evaluating progress towards LF elimination and how different interventions could be leveraged to achieve elimination by 2030. METHODS Here we used a novel approach to combine historical geo-spatial disease prevalence maps of LF in Ethiopia with 3 contemporary disease transmission models to project trends in infection under different intervention scenarios at subnational level. RESULTS Our findings show that local context, particularly the coverage of interventions, is an important determinant for the success of control and elimination programmes. Furthermore, although current strategies seem sufficient to achieve LF elimination by 2030, some areas may benefit from the implementation of alternative strategies, such as using enhanced coverage or increased frequency, to accelerate progress towards the 2030 targets. CONCLUSIONS The combination of geospatial disease prevalence maps of LF with transmission models and intervention histories enables the projection of trends in infection at the subnational level under different control scenarios in Ethiopia. This approach, which adapts transmission models to local settings, may be useful to inform the design of optimal interventions at the subnational level in other LF endemic regions.
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Affiliation(s)
- Joaquin M Prada
- Department of Comparative Biomedical Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | | | - Biruck Kebede
- RTI International, 3040 E Cornwallis Rd, Research Triangle Park, North Carolina 27709, USA
| | | | - Heven Sime
- Malaria and Neglected Tropical Diseases Research Team, Bacterial, Parasitic and Zoonotic Disease Research Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia
| | - Morgan Smith
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | | | - Paul Brown
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Jorge Cano
- Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN), WHO Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
| | - Hajnal Farkas
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Mike Irvine
- Faculty of Science, BC Centre for Disease Control, Vancouver, Canada
| | - Lisa Reimer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Rocio Caja Rivera
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Sake J de Vlas
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Edwin Michael
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Wilma A Stolk
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rachel Pulan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Simon E F Spencer
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - T Déirdre Hollingsworth
- Nuffield Department of Medicine, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Fikre Seife
- Disease Prevention and Control Directorate, Federal Ministry of Health, Addis Ababa, Ethiopia
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7
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Kura K, Stolk WA, Basáñez MG, Collyer BS, de Vlas SJ, Diggle PJ, Gass K, Graham M, Hollingsworth TD, King JD, Krentel A, Anderson RM, Coffeng LE. How Does the Proportion of Never Treatment Influence the Success of Mass Drug Administration Programs for the Elimination of Lymphatic Filariasis? Clin Infect Dis 2024; 78:S93-S100. [PMID: 38662701 PMCID: PMC11045024 DOI: 10.1093/cid/ciae021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Mass drug administration (MDA) is the cornerstone for the elimination of lymphatic filariasis (LF). The proportion of the population that is never treated (NT) is a crucial determinant of whether this goal is achieved within reasonable time frames. METHODS Using 2 individual-based stochastic LF transmission models, we assess the maximum permissible level of NT for which the 1% microfilaremia (mf) prevalence threshold can be achieved (with 90% probability) within 10 years under different scenarios of annual MDA coverage, drug combination and transmission setting. RESULTS For Anopheles-transmission settings, we find that treating 80% of the eligible population annually with ivermectin + albendazole (IA) can achieve the 1% mf prevalence threshold within 10 years of annual treatment when baseline mf prevalence is 10%, as long as NT <10%. Higher proportions of NT are acceptable when more efficacious treatment regimens are used. For Culex-transmission settings with a low (5%) baseline mf prevalence and diethylcarbamazine + albendazole (DA) or ivermectin + diethylcarbamazine + albendazole (IDA) treatment, elimination can be reached if treatment coverage among eligibles is 80% or higher. For 10% baseline mf prevalence, the target can be achieved when the annual coverage is 80% and NT ≤15%. Higher infection prevalence or levels of NT would make achieving the target more difficult. CONCLUSIONS The proportion of people never treated in MDA programmes for LF can strongly influence the achievement of elimination and the impact of NT is greater in high transmission areas. This study provides a starting point for further development of criteria for the evaluation of NT.
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Affiliation(s)
- Klodeta Kura
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Benjamin S Collyer
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Peter J Diggle
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Katherine Gass
- Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, Georgia, USA
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Jonathan D King
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Alison Krentel
- Bruyère Research Institute, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Canada
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, London, United Kingdom
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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8
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Antony Oliver MC, Graham M, Gass KM, Medley GF, Clark J, Davis EL, Reimer LJ, King JD, Pouwels KB, Hollingsworth TD. Reducing the Antigen Prevalence Target Threshold for Stopping and Restarting Mass Drug Administration for Lymphatic Filariasis Elimination: A Model-Based Cost-effectiveness Simulation in Tanzania, India and Haiti. Clin Infect Dis 2024; 78:S160-S168. [PMID: 38662697 PMCID: PMC11045020 DOI: 10.1093/cid/ciae108] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The Global Programme to Eliminate Lymphatic Filariasis (GPELF) aims to reduce and maintain infection levels through mass drug administration (MDA), but there is evidence of ongoing transmission after MDA in areas where Culex mosquitoes are the main transmission vector, suggesting that a more stringent criterion is required for MDA decision making in these settings. METHODS We use a transmission model to investigate how a lower prevalence threshold (<1% antigenemia [Ag] prevalence compared with <2% Ag prevalence) for MDA decision making would affect the probability of local elimination, health outcomes, the number of MDA rounds, including restarts, and program costs associated with MDA and surveys across different scenarios. To determine the cost-effectiveness of switching to a lower threshold, we simulated 65% and 80% MDA coverage of the total population for different willingness to pay per disability-adjusted life-year averted for India ($446.07), Tanzania ($389.83), and Haiti ($219.84). RESULTS Our results suggest that with a lower Ag threshold, there is a small proportion of simulations where extra rounds are required to reach the target, but this also reduces the need to restart MDA later in the program. For 80% coverage, the lower threshold is cost-effective across all baseline prevalences for India, Tanzania, and Haiti. For 65% MDA coverage, the lower threshold is not cost-effective due to additional MDA rounds, although it increases the probability of local elimination. Valuing the benefits of elimination to align with the GPELF goals, we find that a willingness to pay per capita government expenditure of approximately $1000-$4000 for 1% increase in the probability of local elimination would be required to make a lower threshold cost-effective. CONCLUSIONS Lower Ag thresholds for stopping MDAs generally mean a higher probability of local elimination, reducing long-term costs and health impacts. However, they may also lead to an increased number of MDA rounds required to reach the lower threshold and, therefore, increased short-term costs. Collectively, our analyses highlight that lower target Ag thresholds have the potential to assist programs in achieving lymphatic filariasis goals.
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Affiliation(s)
- Mary Chriselda Antony Oliver
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Katherine M Gass
- Neglected Tropical Diseases Support Centre, The Task Force for Global Health, Decatur, Georgia, USA
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Jessica Clark
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Emma L Davis
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiological Research, University of Warwick, Coventry, United Kingdom
| | - Lisa J Reimer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Jonathan D King
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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9
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Rock KS, Chapman LAC, Dobson AP, Adams ER, Hollingsworth TD. The Hidden Hand of Asymptomatic Infection Hinders Control of Neglected Tropical Diseases: A Modeling Analysis. Clin Infect Dis 2024; 78:S175-S182. [PMID: 38662705 PMCID: PMC11045017 DOI: 10.1093/cid/ciae096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Neglected tropical diseases are responsible for considerable morbidity and mortality in low-income populations. International efforts have reduced their global burden, but transmission is persistent and case-finding-based interventions rarely target asymptomatic individuals. METHODS We develop a generic mathematical modeling framework for analyzing the dynamics of visceral leishmaniasis in the Indian sub-continent (VL), gambiense sleeping sickness (gHAT), and Chagas disease and use it to assess the possible contribution of asymptomatics who later develop disease (pre-symptomatics) and those who do not (non-symptomatics) to the maintenance of infection. Plausible interventions, including active screening, vector control, and reduced time to detection, are simulated for the three diseases. RESULTS We found that the high asymptomatic contribution to transmission for Chagas and gHAT and the apparently high basic reproductive number of VL may undermine long-term control. However, the ability to treat some asymptomatics for Chagas and gHAT should make them more controllable, albeit over relatively long time periods due to the slow dynamics of these diseases. For VL, the toxicity of available therapeutics means the asymptomatic population cannot currently be treated, but combining treatment of symptomatics and vector control could yield a quick reduction in transmission. CONCLUSIONS Despite the uncertainty in natural history, it appears there is already a relatively good toolbox of interventions to eliminate gHAT, and it is likely that Chagas will need improvements to diagnostics and their use to better target pre-symptomatics. The situation for VL is less clear, and model predictions could be improved by additional empirical data. However, interventions may have to improve to successfully eliminate this disease.
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Affiliation(s)
- Kat S Rock
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Lloyd A C Chapman
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Andrew P Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Emily R Adams
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - T Déirdre Hollingsworth
- Nuffield Department of Medicine, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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10
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Davis EL, Hollingsworth TD, Keeling MJ. An analytically tractable, age-structured model of the impact of vector control on mosquito-transmitted infections. PLoS Comput Biol 2024; 20:e1011440. [PMID: 38484022 DOI: 10.1371/journal.pcbi.1011440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 03/26/2024] [Accepted: 02/09/2024] [Indexed: 03/27/2024] Open
Abstract
Vector control is a vital tool utilised by malaria control and elimination programmes worldwide, and as such it is important that we can accurately quantify the expected public health impact of these methods. There are very few previous models that consider vector-control-induced changes in the age-structure of the vector population and the resulting impact on transmission. We analytically derive the steady-state solution of a novel age-structured deterministic compartmental model describing the mosquito feeding cycle, with mosquito age represented discretely by parity-the number of cycles (or successful bloodmeals) completed. Our key model output comprises an explicit, analytically tractable solution that can be used to directly quantify key transmission statistics, such as the effective reproductive ratio under control, Rc, and investigate the age-structured impact of vector control. Application of this model reinforces current knowledge that adult-acting interventions, such as indoor residual spraying of insecticides (IRS) or long-lasting insecticidal nets (LLINs), can be highly effective at reducing transmission, due to the dual effects of repelling and killing mosquitoes. We also demonstrate how larval measures can be implemented in addition to adult-acting measures to reduce Rc and mitigate the impact of waning insecticidal efficacy, as well as how mid-ranges of LLIN coverage are likely to experience the largest effect of reduced net integrity on transmission. We conclude that whilst well-maintained adult-acting vector control measures are substantially more effective than larval-based interventions, incorporating larval control in existing LLIN or IRS programmes could substantially reduce transmission and help mitigate any waning effects of adult-acting measures.
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Affiliation(s)
- Emma L Davis
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology, University of Warwick, Coventry, United Kingdom
| | | | - Matt J Keeling
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology, University of Warwick, Coventry, United Kingdom
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11
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Davis EL, Crump RE, Medley GF, Solomon AW, Pemmaraju VRR, Hollingsworth TD. A modelling analysis of a new multi-stage pathway for classifying achievement of public health milestones for leprosy. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220408. [PMID: 37598707 PMCID: PMC10440169 DOI: 10.1098/rstb.2022.0408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/24/2023] [Indexed: 08/22/2023] Open
Abstract
Several countries have come close to eliminating leprosy, but leprosy cases continue to be detected at low levels. Due to the long, highly variable delay from infection to detection, the relationship between observed cases and transmission is uncertain. The World Health Organization's new technical guidance provides a path for countries to reach elimination. We use a simple probabilistic model to simulate the stochastic dynamics of detected cases as transmission declines, and evaluate progress through the new public health milestones. In simulations where transmission is halted, 5 years of zero incidence in autochthonous children, combined with 3 years of zero incidence in all ages is a flawed indicator that transmission has halted (54% correctly classified). A further 10 years of only occasional sporadic cases is associated with a high probability of having interrupted transmission (99%). If, however, transmission continues at extremely low levels, it is possible that cases could be misidentified as historic cases from the tail of the incubation period distribution, although misleadingly achieving all three milestones is unlikely (less than 1% probability across a 15-year period of ongoing low-level transmission). These results demonstrate the feasibility and challenges of a phased progression of milestones towards interruption of transmission, allowing assessment of programme status. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Emma L. Davis
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Ron E. Crump
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Graham F. Medley
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Anthony W. Solomon
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, 1211, Switzerland
| | | | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
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12
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Forbes K, Basáñez MG, Hollingsworth TD, Anderson RM. Introduction to the special issue: challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220272. [PMID: 37598699 PMCID: PMC10440167 DOI: 10.1098/rstb.2022.0272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023] Open
Abstract
Twenty neglected tropical diseases (NTDs) are currently prioritised by the World Health Organization for eradication, elimination as a public health problem, elimination of transmission or control by 2030. This issue celebrates progress made since the 2012 London Declaration on NTDs and discusses challenges currently faced to achieve these goals. It comprises 14 contributions spanning NTDs tackled by intensified disease management to those addressed by preventive chemotherapy. Although COVID-19 negatively affected NTD programmes, it also served to spur new multisectoral approaches to strengthen school-based health systems. The issue highlights the needs to improve impact survey design, evaluate new diagnostics, understand the consequences of heterogeneous prevalence and human movement, the potential impact of alternative treatment strategies and the importance of zoonotic transmission. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Kathryn Forbes
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | | | - Roy M. Anderson
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis (MRC GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
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13
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Clark J, Davis EL, Prada JM, Gass K, Krentel A, Hollingsworth TD. How correlations between treatment access and surveillance inclusion impact neglected tropical disease monitoring and evaluation-A simulated study. PLoS Negl Trop Dis 2023; 17:e0011582. [PMID: 37672518 PMCID: PMC10506705 DOI: 10.1371/journal.pntd.0011582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/18/2023] [Accepted: 08/09/2023] [Indexed: 09/08/2023] Open
Abstract
Neglected tropical diseases (NTDs) largely impact marginalised communities living in tropical and subtropical regions. Mass drug administration is the leading intervention method for five NTDs; however, it is known that there is lack of access to treatment for some populations and demographic groups. It is also likely that those individuals without access to treatment are excluded from surveillance. It is important to consider the impacts of this on the overall success, and monitoring and evaluation (M&E) of intervention programmes. We use a detailed individual-based model of the infection dynamics of lymphatic filariasis to investigate the impact of excluded, untreated, and therefore unobserved groups on the true versus observed infection dynamics and subsequent intervention success. We simulate surveillance in four groups-the whole population eligible to receive treatment, the whole eligible population with access to treatment, the TAS focus of six- and seven-year-olds, and finally in >20-year-olds. We show that the surveillance group under observation has a significant impact on perceived dynamics. Exclusion to treatment and surveillance negatively impacts the probability of reaching public health goals, though in populations that do reach these goals there are no signals to indicate excluded groups. Increasingly restricted surveillance groups over-estimate the efficacy of MDA. The presence of non-treated groups cannot be inferred when surveillance is only occurring in the group receiving treatment.
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Affiliation(s)
- Jessica Clark
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, Scotland
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
| | - Emma L. Davis
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, England
| | - Katherine Gass
- Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, Georgia, United States of America
| | - Alison Krentel
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
| | - T. Déirdre Hollingsworth
- Big Data Institute, Neglected Tropical Disease Modelling Consortium, University of Oxford, Oxford, England
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14
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Anderson RM, Cano J, Hollingsworth TD, Deribe-Kassaye K, Zouré HGM, Kello AB, Impouma B, Kalu AA, Appleby L, Yard E, Salasibew M, McRae-McKee K, Vegvari C. Responding to the cuts in UK AID to neglected tropical diseases control programmes in Africa. Trans R Soc Trop Med Hyg 2023; 117:237-239. [PMID: 36416069 PMCID: PMC9977241 DOI: 10.1093/trstmh/trac109] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/13/2022] [Accepted: 10/27/2022] [Indexed: 11/24/2022] Open
Abstract
The early termination of the Accelerating the Sustainable Control and Elimination of Neglected Tropical Diseases (Ascend) programme by the UK government in June 2021 was a bitter blow to countries in East and West Africa where no alternative source of funding existed. Here we assess the potential impact the cuts may have had if alternative funding had not been made available by new development partners and outline new strategies developed by affected countries to mitigate current and future disruptions to neglected tropical disease control programmes.
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Affiliation(s)
- Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- London Centre for Neglected Tropical Disease Research, London, UK
- Oriole Global Health, London, UK
| | - Jorge Cano
- Expanded Special Project for Elimination of NTDs, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Honorat G M Zouré
- Expanded Special Project for Elimination of NTDs, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo
| | - Amir B Kello
- Expanded Special Project for Elimination of NTDs, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo
| | - Benido Impouma
- Communicable and Non-communicable Disease Cluster, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo
| | - Akpaka A Kalu
- Tropical and vector-borne diseases, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo
| | | | | | | | | | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- London Centre for Neglected Tropical Disease Research, London, UK
- Oriole Global Health, London, UK
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15
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Pan D, Nazareth J, Sze S, Martin CA, Decker J, Fletcher E, Déirdre Hollingsworth T, Barer MR, Pareek M, Tang JW. Transmission of monkeypox/mpox virus: A narrative review of environmental, viral, host, and population factors in relation to the 2022 international outbreak. J Med Virol 2023; 95:e28534. [PMID: 36708091 PMCID: PMC10107822 DOI: 10.1002/jmv.28534] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/05/2022] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Monkeypox virus (MPXV) has spread globally. Emerging studies have now provided evidence regarding MPXV transmission, that can inform rational evidence-based policies and reduce misinformation on this topic. We aimed to review the evidence on transmission of the virus. Real-world studies have isolated viable viruses from high-touch surfaces for as long as 15 days. Strong evidence suggests that the current circulating monkeypox (mpox) has evolved from previous outbreaks outside of Africa, but it is yet unknown whether these mutations may lead to an inherently increased infectivity of the virus. Strong evidence also suggests that the main route of current MPXV transmission is sexual; through either close contact or directly, with detection of culturable virus in saliva, nasopharynx, and sperm for prolonged periods and the presence of rashes mainly in genital areas. The milder clinical presentations and the potential presence of presymptomatic transmission in the current circulating variant compared to previous clades, as well as the dominance of spread amongst men who have sex with men (MSMs) suggests that mpox has a developed distinct clinical phenotype that has increased its transmissibility. Increased public awareness of MPXV transmission modalities may lead to earlier detection of the spillover of new cases into other groups.
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Affiliation(s)
- Daniel Pan
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of Infectious Diseases and HIV MedicineUniversity Hospitals of Leicester NHS TrustLeicesterUK
- Li Ka Shing Centre for Health Information and Discovery, Big Data InstituteUniversity of OxfordOxfordUK
- NIHR Leicester Biomedical Research CentreLiecesterUK
| | - Joshua Nazareth
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of Infectious Diseases and HIV MedicineUniversity Hospitals of Leicester NHS TrustLeicesterUK
- NIHR Leicester Biomedical Research CentreLiecesterUK
| | - Shirley Sze
- Department of Cardiovascular SciencesUniversity of LeicesterLeicesterUK
| | - Christopher A. Martin
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of Infectious Diseases and HIV MedicineUniversity Hospitals of Leicester NHS TrustLeicesterUK
- NIHR Leicester Biomedical Research CentreLiecesterUK
| | - Jonathan Decker
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
| | - Eve Fletcher
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
| | - T. Déirdre Hollingsworth
- Li Ka Shing Centre for Health Information and Discovery, Big Data InstituteUniversity of OxfordOxfordUK
| | - Michael R. Barer
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of Clinical MicrobiologyUniversity Hospitals of Leicester NHS TrustLeicesterUK
| | - Manish Pareek
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of Infectious Diseases and HIV MedicineUniversity Hospitals of Leicester NHS TrustLeicesterUK
- NIHR Leicester Biomedical Research CentreLiecesterUK
| | - Julian W. Tang
- Department of Respiratory SciencesUniversity of LeicesterLeicesterUK
- Department of VirologyUniversity Hospitals of Leicester NHS TrustLeicesterUK
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16
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Crellen T, Haswell M, Sithithaworn P, Sayasone S, Odermatt P, Lamberton PHL, Spencer SEF, Déirdre Hollingsworth T. Diagnosis of helminths depends on worm fecundity and the distribution of parasites within hosts. Proc Biol Sci 2023; 290:20222204. [PMID: 36651047 PMCID: PMC9845982 DOI: 10.1098/rspb.2022.2204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/16/2022] [Indexed: 01/19/2023] Open
Abstract
Helminth transmission and morbidity are dependent on the number of mature parasites within a host; however, observing adult worms is impossible for many natural infections. An outstanding challenge is therefore relating routine diagnostics, such as faecal egg counts, to the underlying worm burden. This relationship is complicated by density-dependent fecundity (egg output per worm reduces due to crowding at high burdens) and the skewed distribution of parasites (majority of helminths aggregated in a small fraction of hosts). We address these questions for the carcinogenic liver fluke Opisthorchis viverrini, which infects approximately 10 million people across Southeast Asia, by analysing five epidemiological surveys (n = 641) where adult flukes were recovered. Using a mechanistic model, we show that parasite fecundity varies between populations, with surveys from Thailand and Laos demonstrating distinct patterns of egg output and density-dependence. As the probability of observing faecal eggs increases with the number of mature parasites within a host, we quantify diagnostic sensitivity as a function of the worm burden and find that greater than 50% of cases are misdiagnosed as false negative in communities close to elimination. Finally, we demonstrate that the relationship between observed prevalence from routine diagnostics and true prevalence is nonlinear and strongly influenced by parasite aggregation.
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Affiliation(s)
- Thomas Crellen
- School of Biodiversity One Health and Veterinary Medicine, Graham Kerr Building, University of Glasgow, 82 Hillhead Street, Glasgow G12 8QQ, UK
- Wellcome Centre for Integrative Parasitology, Sir Graeme Davies Building, University of Glasgow, 120 University Place, Glasgow G12 8TA, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Melissa Haswell
- Office of the Deputy Vice Chancellor, Indigenous Strategy and Services and School of Geosciences, John Woolley Building, University of Sydney, Sydney, New South Wales 2050, Australia
- School of Public Health and Social Work, Kelvin Grove Campus, Queensland University of Technology, Brisbane City, Queensland 4000, Australia
| | - Paiboon Sithithaworn
- Department of Parasitology, Khon Kaen University, 123 Thanon Mittraphap, Khon Kaen 40002, Thailand
| | - Somphou Sayasone
- Lao Tropical and Public Health Institute, Samsenthai Road, Sisattanak district, Vientiane, Lao PDR
| | - Peter Odermatt
- Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwil 4123, Switzerland
- University of Basel, Petersplatz 1, Basel 4001, Switzerland
| | - Poppy H. L. Lamberton
- School of Biodiversity One Health and Veterinary Medicine, Graham Kerr Building, University of Glasgow, 82 Hillhead Street, Glasgow G12 8QQ, UK
- Wellcome Centre for Integrative Parasitology, Sir Graeme Davies Building, University of Glasgow, 120 University Place, Glasgow G12 8TA, UK
| | | | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
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17
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Dangerfield CE, David Abrahams I, Budd C, Butchers M, Cates ME, Champneys AR, Currie CS, Enright J, Gog JR, Goriely A, Déirdre Hollingsworth T, Hoyle RB, INI Professional Services, Isham V, Jordan J, Kaouri MH, Kavoussanakis K, Leeks J, Maini PK, Marr C, Merritt C, Mollison D, Ray S, Thompson RN, Wakefield A, Wasley D. Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK. J Theor Biol 2023; 557:111332. [PMID: 36323393 PMCID: PMC9618296 DOI: 10.1016/j.jtbi.2022.111332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Abstract
In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Ciara E. Dangerfield
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom,Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Corresponding author
| | - I. David Abrahams
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Chris Budd
- Department of Mathematics, University of Bath, United Kingdom
| | - Matt Butchers
- Department of Mathematics, University of Bath, United Kingdom
| | - Michael E. Cates
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Alan R. Champneys
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | | | - Jessica Enright
- School of Computing Science, University of Glasgow, United Kingdom
| | - Julia R. Gog
- Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
| | - Alain Goriely
- Mathematical Institute, University of Oxford, United Kingdom
| | - T. Déirdre Hollingsworth
- Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom1,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - Rebecca B. Hoyle
- School of Mathematical Sciences, University of Southampton, United Kingdom
| | | | - Valerie Isham
- Department of Statistical Science, University College London, United Kingdom
| | | | - Maha H. Kaouri
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | | | - Jane Leeks
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Philip K. Maini
- Mathematical Institute, University of Oxford, United Kingdom
| | - Christie Marr
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Clare Merritt
- Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, United Kingdom
| | - Surajit Ray
- School of Mathematics and Statistics, University of Glasgow, United Kingdom
| | - Robin N. Thompson
- Mathematics Institute, University of Warwick, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, United Kingdom
| | | | - Dawn Wasley
- International Centre for Mathematical Sciences, University of Edinburgh & Heriot-Watt University, United Kingdom
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18
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Baggaley RF, Vegvari C, Dimala CA, Lipman M, Miller RF, Brown J, Degtyareva S, White HA, Hollingsworth TD, Pareek M. Health economic analyses of latent tuberculosis infection screening and preventive treatment among people living with HIV in lower tuberculosis incidence settings: a systematic review. Wellcome Open Res 2023; 6:51. [PMID: 37025515 PMCID: PMC10071141.2 DOI: 10.12688/wellcomeopenres.16604.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2022] [Indexed: 01/07/2023] Open
Abstract
Introduction: In lower tuberculosis (TB) incidence countries (<100 cases/100,000/year), screening and preventive treatment (PT) for latent TB infection (LTBI) among people living with HIV (PLWH) is often recommended, yet guidelines advising which groups to prioritise for screening can be contradictory and implementation patchy. Evidence of LTBI screening cost-effectiveness may improve uptake and health outcomes at reasonable cost. Methods: Our systematic review assessed cost-effectiveness estimates of LTBI screening/PT strategies among PLWH in lower TB incidence countries to identify model-driving inputs and methodological differences. Databases were searched 1980-2020. Studies including health economic evaluation of LTBI screening of PLWH in lower TB incidence countries (<100 cases/100,000/year) were included. Results: Of 2,644 articles screened, nine studies were included. Cost-effectiveness estimates of LTBI screening/PT for PLWH varied widely, with universal screening/PT found highly cost-effective by some studies, while only targeting to high-risk groups (such as those from mid/high TB incidence countries) deemed cost-effective by others. Cost-effectiveness of strategies screening all PLWH from studies published in the past five years varied from US$2828 to US$144,929/quality-adjusted life-year gained (2018 prices). Study quality varied, with inconsistent reporting of methods and results limiting comparability of studies. Cost-effectiveness varied markedly by screening guideline, with British HIV Association guidelines more cost-effective than NICE guidelines in the UK. Discussion: Cost-effectiveness studies of LTBI screening/PT for PLWH in lower TB incidence settings are scarce, with large variations in methods and assumptions used, target populations and screening/PT strategies evaluated. The limited evidence suggests LTBI screening/PT may be cost-effective for some PLWH groups but further research is required, particularly on strategies targeting screening/PT to PLWH at higher risk. Standardisation of model descriptions and results reporting could facilitate reliable comparisons between studies, particularly to identify those factors driving the wide disparity between cost-effectiveness estimates. Registration: PROSPERO CRD42020166338 (18/03/2020).
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Affiliation(s)
- Rebecca F. Baggaley
- Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- UCL Respiratory, University College London, London, UK
| | - Christian A. Dimala
- Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Marc Lipman
- Royal Free London National Health Service Foundation Trust, London, UK
- RUDN University, Moscow, Russian Federation
| | | | | | - Svetlana Degtyareva
- Department of Infection and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | | | | | - Manish Pareek
- Big Data Institute, University of Oxford, Oxford, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, LE1 7RH, UK
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19
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Stolk WA, Coffeng LE, Bolay FK, Eneanya OA, Fischer PU, Hollingsworth TD, Koudou BG, Méité A, Michael E, Prada JM, Caja Rivera RM, Sharma S, Touloupou P, Weil GJ, de Vlas SJ. Comparing antigenaemia- and microfilaraemia as criteria for stopping decisions in lymphatic filariasis elimination programmes in Africa. PLoS Negl Trop Dis 2022; 16:e0010953. [PMID: 36508458 PMCID: PMC9779720 DOI: 10.1371/journal.pntd.0010953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/22/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Mass drug administration (MDA) is the main strategy towards lymphatic filariasis (LF) elimination. Progress is monitored by assessing microfilaraemia (Mf) or circulating filarial antigenaemia (CFA) prevalence, the latter being more practical for field surveys. The current criterion for stopping MDA requires <2% CFA prevalence in 6- to 7-year olds, but this criterion is not evidence-based. We used mathematical modelling to investigate the validity of different thresholds regarding testing method and age group for African MDA programmes using ivermectin plus albendazole. METHODOLGY/PRINCIPAL FINDINGS We verified that our model captures observed patterns in Mf and CFA prevalence during annual MDA, assuming that CFA tests are positive if at least one adult worm is present. We then assessed how well elimination can be predicted from CFA prevalence in 6-7-year-old children or from Mf or CFA prevalence in the 5+ or 15+ population, and determined safe (>95% positive predictive value) thresholds for stopping MDA. The model captured trends in Mf and CFA prevalences reasonably well. Elimination cannot be predicted with sufficient certainty from CFA prevalence in 6-7-year olds. Resurgence may still occur if all children are antigen-negative, irrespective of the number tested. Mf-based criteria also show unfavourable results (PPV <95% or unpractically low threshold). CFA prevalences in the 5+ or 15+ population are the best predictors, and post-MDA threshold values for stopping MDA can be as high as 10% for 15+. These thresholds are robust for various alternative assumptions regarding baseline endemicity, biological parameters and sampling strategies. CONCLUSIONS/SIGNIFICANCE For African areas with moderate to high pre-treatment Mf prevalence that have had 6 or more rounds of annual ivermectin/albendazole MDA with adequate coverage, we recommend to adopt a CFA threshold prevalence of 10% in adults (15+) for stopping MDA. This could be combined with Mf testing of CFA positives to ensure absence of a significant Mf reservoir for transmission.
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Affiliation(s)
- Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- * E-mail:
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Fatorma K. Bolay
- National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | - Obiora A. Eneanya
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Peter U. Fischer
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Benjamin G. Koudou
- Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Abidjan, Côte d’Ivoire
- Laboratoire de Cytologie et Biologie Animale, UFR Science de la Nature, Université Nangui Abrogoua Abidjan, Abidjan, Côte d’Ivoire
| | - Aboulaye Méité
- Programme National de Lutte contre les Maladies Tropicales Négligées à Chimiothérapie Préventive, Abidjan, Côte d’Ivoire
| | - Edwin Michael
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Joaquin M. Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Rocio M. Caja Rivera
- Center for Global Health Infectious Disease Research, University of South Florida, Tampa, Florida, United States of America
| | - Swarnali Sharma
- Department of Biological Sciences, University of Notre Dame, South Bend, Indiana, United States of America
- Christian Medical College, IDA Scudder Rd, Vellore, Tamil Nadu, India
| | - Panayiota Touloupou
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Gary J. Weil
- Infectious Diseases Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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20
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Quaife M, Medley GF, Jit M, Drake T, Asaria M, van Baal P, Baltussen R, Bollinger L, Bozzani F, Brady O, Broekhuizen H, Chalkidou K, Chi YL, Dowdy DW, Griffin S, Haghparast-Bidgoli H, Hallett T, Hauck K, Hollingsworth TD, McQuaid CF, Menzies NA, Merritt MW, Mirelman A, Morton A, Ruiz FJ, Siapka M, Skordis J, Tediosi F, Walker P, White RG, Winskill P, Vassall A, Gomez GB. Considering equity in priority setting using transmission models: Recommendations and data needs. Epidemics 2022; 41:100648. [PMID: 36343495 PMCID: PMC9623400 DOI: 10.1016/j.epidem.2022.100648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. METHODS We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. RESULTS We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. CONCLUSIONS Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.
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Affiliation(s)
- M. Quaife
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - GF Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - M. Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - T. Drake
- Center for Global Development in Europe (CGD Europe), UK
| | - M. Asaria
- LSE Health, London School of Economics, UK
| | - P. van Baal
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, the Netherlands
| | - R. Baltussen
- Nijmegen International Center for Health Systems Research and Education, Radboudmc, the Netherlands
| | | | - F. Bozzani
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - O. Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - H. Broekhuizen
- Centre for Space, Place, and Society, Wageningen University and Research, Netherlands
| | - K. Chalkidou
- International Decision Support Initiative, Imperial College London, UK
| | - Y.-L. Chi
- International Decision Support Initiative, Imperial College London, UK
| | - DW Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USA
| | - S. Griffin
- Centre for Health Economics, University of York, UK
| | - H. Haghparast-Bidgoli
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - T. Hallett
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - K. Hauck
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - TD Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - CF McQuaid
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - NA Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, USA
| | - MW Merritt
- Johns Hopkins Berman Institute of Bioethics and Department of International Health, Johns Hopkins Bloomberg School of Public Health, United States
| | - A. Mirelman
- Centre for Health Economics, University of York, UK
| | - A. Morton
- Department of Management Science, University of Strathclyde, UK
| | - FJ Ruiz
- International Decision Support Initiative, Imperial College London, UK
| | - M. Siapka
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Impact Elipsis, Greece
| | - J. Skordis
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - F. Tediosi
- Swiss Tropical and Public Health Institute and Universität Basel, Switzerland
| | - P. Walker
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - RG White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - P. Winskill
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - A. Vassall
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Correspondence to: London School of Hygiene and Tropical Medicine, 15 – 17 Tavistock Place, London WC1H 9SH, UK
| | - GB Gomez
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
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21
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Pan D, Sze S, Nazareth J, Martin CA, Al-Oraibi A, Baggaley RF, Nellums LB, Hollingsworth TD, Tang JW, Pareek M. The monkeypox case definition in the UK is broad - Authors' reply. Lancet 2022; 400:1301-1302. [PMID: 36244375 PMCID: PMC9560763 DOI: 10.1016/s0140-6736(22)01807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Daniel Pan
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Shirley Sze
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE1 9HN, UK
| | - Joshua Nazareth
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Christopher A Martin
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Amani Al-Oraibi
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Rebecca F Baggaley
- Department of Health Sciences, University of Leicester, Leicester LE1 9HN, UK
| | - Laura B Nellums
- Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Julian W Tang
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Virology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Manish Pareek
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK.
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22
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Vegvari C, Abbott S, Ball F, Brooks-Pollock E, Challen R, Collyer BS, Dangerfield C, Gog JR, Gostic KM, Heffernan JM, Hollingsworth TD, Isham V, Kenah E, Mollison D, Panovska-Griffiths J, Pellis L, Roberts MG, Scalia Tomba G, Thompson RN, Trapman P. Commentary on the use of the reproduction number R during the COVID-19 pandemic. Stat Methods Med Res 2022; 31:1675-1685. [PMID: 34569883 PMCID: PMC9277711 DOI: 10.1177/09622802211037079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.
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Affiliation(s)
- Carolin Vegvari
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | - Sam Abbott
- Center for the Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene & Tropical Medicine, UK
| | - Frank Ball
- School of Mathematical Sciences, 6123University of Nottingham, UK
| | - Ellen Brooks-Pollock
- Bristol Veterinary School, 1980University of Bristol, UK.,NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, UK
| | - Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK.,Somerset NHS Foundation Trust, UK
| | - Benjamin S Collyer
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | | | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
| | - Katelyn M Gostic
- Department of Ecology and Evolution, 2462University of Chicago, USA
| | - Jane M Heffernan
- Centre for Disease Modelling, Mathematics & Statistics, 7991York University, Canada.,COVID Modelling Task-Force, The Fields Institute, Canada
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, 6396University of Oxford, UK
| | - Valerie Isham
- Department of Statistical Science, 4919University College London, UK
| | - Eben Kenah
- Division of Biostatistics, College of Public Health, 2647The Ohio State University, USA
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Wolfson Centre for Mathematical Biology, Mathematical Institute and The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292The University of Manchester, UK.,The Alan Turing Institute, UK
| | - Michael G Roberts
- School of Natural and Computational Sciences and New Zealand Institute for Advanced Study, Massey University, New Zealand
| | | | - Robin N Thompson
- Mathematics Institute, 2707University of Warwick, Coventry, UK.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, 2707University of Warwick, Coventry, UK
| | - Pieter Trapman
- Department of Mathematics, 7675Stockholm University, Sweden
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23
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Pan D, Sze S, Nazareth J, Martin CA, Al-Oraibi A, Baggaley RF, Nellums LB, Hollingsworth TD, Tang JW, Pareek M. Monkeypox in the UK: arguments for a broader case definition. Lancet 2022; 399:2345-2346. [PMID: 35716671 PMCID: PMC9528227 DOI: 10.1016/s0140-6736(22)01101-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/12/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Daniel Pan
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Shirley Sze
- Department of Life Sciences, University of Leicester, Leicester LE1 9HN, UK
| | - Joshua Nazareth
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Christopher A Martin
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Amani Al-Oraibi
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Rebecca F Baggaley
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Life Sciences, University of Leicester, Leicester LE1 9HN, UK
| | - Laura B Nellums
- Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Julian W Tang
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Virology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Manish Pareek
- Department of Respiratory Sciences, University of Leicester, Leicester LE1 9HN, UK; Department of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK.
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24
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Kumar V, Siddiqui NA, Pollington TM, Mandal R, Das S, Kesari S, Das VR, Pandey K, Hollingsworth TD, Chapman LA, Das P. Impact of intensified control on visceral leishmaniasis in a highly-endemic district of Bihar, India: An interrupted time series analysis. Epidemics 2022; 39:100562. [PMID: 35561500 PMCID: PMC9188270 DOI: 10.1016/j.epidem.2022.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/16/2021] [Accepted: 04/04/2022] [Indexed: 11/22/2022] Open
Abstract
Visceral leishmaniasis (VL) is declining in India and the World Health Organization’s (WHO) 2020 ‘elimination as a public health problem’ target has nearly been achieved. Intensified combined interventions might help reach elimination, but their impact has not been assessed. WHO’s Neglected Tropical Diseases 2021–2030 roadmap provides an opportunity to revisit VL control strategies. We estimated the combined effect of a district-wide pilot of intensified interventions in the highly-endemic Vaishali district, where cases fell from 3,598 in 2012–2014 to 762 in 2015–2017. The intensified control approach comprised indoor residual spraying with improved supervision; VL-specific training for accredited social health activists to reduce onset-to-diagnosis time; and increased Information Education & Communication activities in the community. We compared the rate of incidence decrease in Vaishali to other districts in Bihar state via an interrupted time series analysis with a spatiotemporal model informed by previous VL epidemiological estimates. Changes in Vaishali’s rank among Bihar’s endemic districts in terms of monthly incidence showed a change pre-pilot (3rd highest out of 33 reporting districts) vs. during the pilot (9th) (p<1e-10). The rate of decline in Vaishali’s incidence saw no change in rank at 11th highest, both pre-pilot & during the pilot. Counterfactual model simulations suggest an estimated median of 352 cases (IQR 234–477) were averted by the Vaishali pilot between January 2015 and December 2017, which was robust to modest changes in the onset-to-diagnosis distribution. Strengthening control strategies may have precipitated a substantial change in VL incidence in Vaishali and suggests this approach should be piloted in other highly-endemic districts.
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25
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Anderson RM, Vegvari C, Hollingsworth TD, Pi L, Maddren R, Ng CW, Baggaley RF. The SARS-CoV-2 pandemic: remaining uncertainties in our understanding of the epidemiology and transmission dynamics of the virus, and challenges to be overcome. Interface Focus 2021; 11:20210008. [PMID: 34956588 PMCID: PMC8504893 DOI: 10.1098/rsfs.2021.0008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2021] [Indexed: 12/11/2022] Open
Abstract
Great progress has been made over the past 18 months in scientific understanding of the biology, epidemiology and pathogenesis of SARS-CoV-2. Extraordinary advances have been made in vaccine development and the execution of clinical trials of possible therapies. However, uncertainties remain, and this review assesses these in the context of virus transmission, epidemiology, control by social distancing measures and mass vaccination and the effect on all of these on emerging variants. We briefly review the current state of the global pandemic, focussing on what is, and what is not, well understood about the parameters that control viral transmission and make up the constituent parts of the basic reproductive number R 0. Major areas of uncertainty include factors predisposing to asymptomatic infection, the population fraction that is asymptomatic, the infectiousness of asymptomatic compared to symptomatic individuals, the contribution of viral transmission of such individuals and what variables influence this. The duration of immunity post infection and post vaccination is also currently unknown, as is the phenotypic consequences of continual viral evolution and the emergence of many viral variants not just in one location, but globally, given the high connectivity between populations in the modern world. The pattern of spread of new variants is also examined. We review what can be learnt from contact tracing, household studies and whole-genome sequencing, regarding where people acquire infection, and how households are seeded with infection since they constitute a major location for viral transmission. We conclude by discussing the challenges to attaining herd immunity, given the uncertainty in the duration of vaccine-mediated immunity, the threat of continued evolution of the virus as demonstrated by the emergence and rapid spread of the Delta variant, and the logistics of vaccine manufacturing and delivery to achieve universal coverage worldwide. Significantly more support from higher income countries (HIC) is required in low- and middle-income countries over the coming year to ensure the creation of community-wide protection by mass vaccination is a global target, not one just for HIC. Unvaccinated populations create opportunities for viral evolution since the net rate of evolution is directly proportional to the number of cases occurring per unit of time. The unit for assessing success in achieving herd immunity is not any individual country, but the world.
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Affiliation(s)
- Roy M. Anderson
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, University of Leicester, Leicester, UK
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, University of Leicester, Leicester, UK
| | - Rosie Maddren
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Chi Wai Ng
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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26
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Retkute R, Touloupou P, Basáñez MG, Hollingsworth TD, Spencer SEF. Integrating geostatistical maps and infectious disease transmission models using adaptive multiple importance sampling. Ann Appl Stat 2021. [DOI: 10.1214/21-aoas1486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Renata Retkute
- Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge
| | | | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research and MRC Centre for Global Infectious Disease Analysis, Faculty of Medicine, School of Public Health, Imperial College London
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health, Information and Discovery, University of Oxford
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27
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Pan D, Sze S, Martin CA, Nazareth J, Woolf K, Baggaley RF, Hollingsworth TD, Khunti K, Nellums LB, Pareek M. Covid-19 and ethnicity: we must seek to understand the drivers of higher transmission. BMJ 2021; 375:n2709. [PMID: 34740938 DOI: 10.1136/bmj.n2709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Daniel Pan
- Department of Respiratory Sciences, University of Leicester
| | - Shirley Sze
- Department of cardiovascular science, University of Leicester
| | | | | | | | | | | | - Kamlesh Khunti
- Department of cardiovascular science, University of Leicester
| | | | - Manish Pareek
- Department of Respiratory Sciences, University of Leicester
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28
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Crellen T, Pi L, Davis EL, Pollington TM, Lucas TCD, Ayabina D, Borlase A, Toor J, Prem K, Medley GF, Klepac P, Déirdre Hollingsworth T. Dynamics of SARS-CoV-2 with waning immunity in the UK population. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200274. [PMID: 34053264 PMCID: PMC8165597 DOI: 10.1098/rstb.2020.0274] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 12/15/2022] Open
Abstract
The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48-142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than 1 year. Using an age-structured, deterministic model, we explore potential immunity dynamics using contact data from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalized individuals, a year for hospitalized individuals, and the effective reproduction number after lockdown ends is 1.2 (our worst-case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 387 000 infectious individuals and 125 000 daily new cases; threefold greater than in a scenario with permanent immunity. Our models suggest that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown in June until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 is expected to increase due to the secondary wave. Overall, our analysis presents considerations for policy makers on the longer-term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection as immunity to reinfection is not permanent. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Thomas Crellen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Emma L. Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- MathSys CDT, University of Warwick, Coventry CV4 7AL, UK
| | - Tim C. D. Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Diepreye Ayabina
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Anna Borlase
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Kiesha Prem
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Graham F. Medley
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Petra Klepac
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
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29
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Hatherell HA, Simpson H, Baggaley RF, Hollingsworth TD, Pullan RL. Sustainable Surveillance of Neglected Tropical Diseases for the Post-Elimination Era. Clin Infect Dis 2021; 72:S210-S216. [PMID: 33977302 PMCID: PMC8201586 DOI: 10.1093/cid/ciab211] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
The World Health Organization’s (WHO’s) 2030 road map for neglected tropical diseases (NTDs) emphasizes the importance of strengthened, institutionalized “post-elimination” surveillance. The required shift from disease-siloed, campaign-based programming to routine, integrated surveillance and response activities presents epidemiological, logistical, and financial challenges, yet practical guidance on implementation is lacking. Nationally representative survey programs, such as demographic and health surveys (DHS), may offer a platform for the integration of NTD surveillance within national health systems and health information systems. Here, we describe characteristics of DHS and other surveys conducted within the WHO Africa region in terms of frequency, target populations, and sample types and discuss applicability for post-validation and post-elimination surveillance. Maximizing utility depends not only on the availability of improved diagnostics but also on better understanding of the spatial and temporal dynamics of transmission at low prevalence. To this end, we outline priorities for obtaining additional data to better characterize optimal post-elimination surveillance platforms.
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Affiliation(s)
- Hollie-Ann Hatherell
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Hope Simpson
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rebecca F Baggaley
- Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | | | - Rachel L Pullan
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom
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30
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Davis EL, Prada J, Reimer LJ, Hollingsworth TD. Modelling the Impact of Vector Control on Lymphatic Filariasis Programs: Current Approaches and Limitations. Clin Infect Dis 2021; 72:S152-S157. [PMID: 33905475 PMCID: PMC8201547 DOI: 10.1093/cid/ciab191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Vector control is widely considered an important tool for lymphatic filariasis (LF) elimination but is not usually included in program budgets and has often been secondary to other policy questions in modelling studies. Evidence from the field demonstrates that vector control can have a large impact on program outcomes and even halt transmission entirely, but implementation is expensive. Models of LF have the potential to inform where and when resources should be focused, but often simplify vector dynamics and focus on capturing human prevalence trends, making them comparatively ill-designed for direct analysis of vector control measures. We review the recent modelling literature and present additional results using a well-established model, highlighting areas of agreement between model predictions and field evidence, and discussing the possible determinants of existing disagreements. We conclude that there are likely to be long-term benefits of vector control, both on accelerating programs and preventing resurgence.
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Affiliation(s)
- E L Davis
- Big Data Institute, University of Oxford, Oxford, UK
| | - J Prada
- University of Surrey, Guildford,UK
| | - L J Reimer
- Liverpool School of Tropical Medicine, Liverpool,UK
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31
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Abstract
As programs move closer toward the World Health Organization (WHO) goals of reduction in morbidity, elimination as a public health problem or elimination of transmission, countries will be faced with planning the next stages of surveillance and control in low prevalence settings. Mathematical models of neglected tropical diseases (NTDs) will need to go beyond predicting the effect of different treatment programs on these goals and on to predicting whether the gains can be sustained. One of the most important challenges will be identifying the policy goal and the right constraints on interventions and surveillance over the long term, as a single policy option will not achieve all aims—for example, minimizing morbidity and minimizing costs cannot both be achieved. As NTDs move toward 2030 and beyond, more nuanced intervention choices will be informed by quantitative analyses which are adapted to national context.
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Affiliation(s)
- Amanda Minter
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, United Kingdom.,The Alan Turing Institute, London, United Kingdom
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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32
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Mabey D, Agler E, Amuasi JH, Hernandez L, Hollingsworth TD, Hotez PJ, Lammie PJ, Malecela MN, Matendechero SH, Ottesen E, Phillips RO, Reeder JC, Szwarcwald CL, Shott JP, Solomon AW, Steer A, Swaminathan S. Towards a comprehensive research and development plan to support the control, elimination and eradication of neglected tropical diseases. Trans R Soc Trop Med Hyg 2021; 115:196-199. [PMID: 33179054 PMCID: PMC7842110 DOI: 10.1093/trstmh/traa114] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/06/2020] [Indexed: 12/14/2022] Open
Abstract
To maximise the likelihood of success, global health programmes need repeated, honest appraisal of their own weaknesses, with research undertaken to address any identified gaps. There is still much to be learned to optimise work against neglected tropical diseases. To facilitate that learning, a comprehensive research and development plan is required. Here, we discuss how such a plan might be developed.
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Affiliation(s)
- David Mabey
- Clinical Research Department, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | | | - John H Amuasi
- African Research Network for Neglected Tropical Diseases, Kumasi AK-039-5028, Ghana
| | - Leda Hernandez
- Department of Health, Infectious Disease Office, National Center for Disease Prevention and Control, Manila 1003, Philippines
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Peter J Hotez
- Departments of Pediatrics and Molecular Virology & Microbiology, Texas Children's Hospital Center for Vaccine Development, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030s, USA.,Hagler Institute for Advanced Study at Texas A & M University, College Station, TX 77843, USA.,Department of Biology, Baylor University, Waco, TX 76706, USA.,James A. Baker III Institute of Public Policy, Rice University, Houston, TX 77005, USA.,Scowcroft Institute of International Affairs, Bush School of Government and Public Service, Texas A & M University, College Station, TX 77845, USA
| | - Patrick J Lammie
- Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, GA 30030, USA
| | - Mwelecele N Malecela
- Department of Control of Neglected Tropical Diseases, WHO 1211, Geneva, Switzerland
| | - Sultani H Matendechero
- Division of Communicable Disease Prevention and Control, Neglected Tropical Diseases Unit, Ministry of Health, Nairobi, Kenya
| | - Eric Ottesen
- Neglected Tropical Diseases Support Center, Task Force for Global Health, Decatur, GA 30030, USA
| | - Richard O Phillips
- Kumasi Center for Collaborative Research in Tropical Medicine, Kwame Nkrumah University of Science and Technology, Kumasi AK-039-5028, Ghana
| | - John C Reeder
- UNICEF, UNDP, World Bank, WHO Special Programme for Research and Training in Tropical Disease (TDR), 1211 Geneva 21040-900, Switzerland
| | - Célia Landmann Szwarcwald
- Instituto de Comunicação e Informação Científica e Tecnológica em Saúde, Fundação Instituto Oswaldo Cruz, Rio de Janeiro, RJ, Brasil
| | - Joseph P Shott
- Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC 20004, USA
| | - Anthony W Solomon
- Department of Control of Neglected Tropical Diseases, WHO 1211, Geneva, Switzerland
| | - Andrew Steer
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria 3010, Australia.,Department of General Medicine, Royal Children's Hospital, Melbourne, Victoria 3052, Australia.,Tropical Diseases Research Group, Murdoch Children's Research Institute, Melbourne, Victoria 3052, Australia
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33
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Lucas TCD, Davis EL, Ayabina D, Borlase A, Crellen T, Pi L, Medley GF, Yardley L, Klepac P, Gog J, Déirdre Hollingsworth T. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200270. [PMID: 34053257 PMCID: PMC8165588 DOI: 10.1098/rstb.2020.0270] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritized. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies which increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
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Affiliation(s)
- Tim C D Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Diepreye Ayabina
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Anna Borlase
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Thomas Crellen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Graham F Medley
- MathSys CDT, University of Warwick, Coventry, UK.,Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Lucy Yardley
- School of Psychology, University of Southampton, Southampton, UK.,School of Psychological Science, University of Bristol, Bristol, UK
| | - Petra Klepac
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.,Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julia Gog
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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Abstract
Analytical expressions and approximations from simple models have performed a pivotal role in our understanding of infectious disease epidemiology. During the current COVID-19 pandemic, while there has been proliferation of increasingly complex models, still the most basic models have provided the core framework for our thinking and interpreting policy decisions. Here, classic results are presented that give insights into both the role of transmission-reducing interventions (such as social distancing) in controlling an emerging epidemic, and also what would happen if insufficient control is applied. Though these are simple results from the most basic of epidemic models, they give valuable benchmarks for comparison with the outputs of more complex modelling approaches. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
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Toor J, Hamley JID, Fronterre C, Castaño MS, Chapman LAC, Coffeng LE, Giardina F, Lietman TM, Michael E, Pinsent A, Le Rutte EA, Hollingsworth TD. Strengthening data collection for neglected tropical diseases: What data are needed for models to better inform tailored intervention programmes? PLoS Negl Trop Dis 2021; 15:e0009351. [PMID: 33983937 PMCID: PMC8118349 DOI: 10.1371/journal.pntd.0009351] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Locally tailored interventions for neglected tropical diseases (NTDs) are becoming increasingly important for ensuring that the World Health Organization (WHO) goals for control and elimination are reached. Mathematical models, such as those developed by the NTD Modelling Consortium, are able to offer recommendations on interventions but remain constrained by the data currently available. Data collection for NTDs needs to be strengthened as better data are required to indirectly inform transmission in an area. Addressing specific data needs will improve our modelling recommendations, enabling more accurate tailoring of interventions and assessment of their progress. In this collection, we discuss the data needs for several NTDs, specifically gambiense human African trypanosomiasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), trachoma, and visceral leishmaniasis. Similarities in the data needs for these NTDs highlight the potential for integration across these diseases and where possible, a wider spectrum of diseases.
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Affiliation(s)
- Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
- * E-mail:
| | - Jonathan I. D. Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Claudio Fronterre
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - María Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Lloyd A. C. Chapman
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, United Kingdom
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Federica Giardina
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Thomas M. Lietman
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America
- Department of Ophthalmology, University of California, San Francisco, California, United States of America
- Department of Epidemiology & Biostatistics, University of California, San Francisco, California, United States of America
| | - Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Amy Pinsent
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Epke A. Le Rutte
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
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36
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Pollington TM, Tildesley MJ, Hollingsworth TD, Chapman LA. Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic. Spat Stat 2021; 42:100438. [PMID: 33816096 PMCID: PMC7985614 DOI: 10.1016/j.spasta.2020.100438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/06/2020] [Accepted: 03/07/2020] [Indexed: 06/12/2023]
Abstract
The tau statistic τ uses geolocation and, usually, symptom onset time to assess global spatiotemporal clustering from epidemiological data. We test different methods that could bias the clustering range estimate based on the statistic or affect its apparent precision, by comparison with a baseline analysis of an open access measles dataset. From re-analysing this data we find evidence against no clustering and no inhibition, p -value ∈ [ 0 , 0 ⋅ 022 ] (global envelope test). We develop a tau-specific modification of the Loh & Stein spatial bootstrap sampling method, which gives bootstrap tau estimates with 24% lower sampling error and a 110% higher estimated clustering endpoint than previously published (61⋅0 m vs. 29 m) and an equivalent increase in the clustering area of elevated disease odds by 342%. These differences could have important consequences for control efforts. Correct practice of graphical hypothesis testing of no clustering and clustering range estimation of the tau statistic are illustrated in the online Graphical abstract. We advocate proper implementation of this useful statistic, ultimately to reduce inaccuracies in control policy decisions made during disease clustering analysis.
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Affiliation(s)
- Timothy M. Pollington
- MathSys CDT, University of Warwick, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Michael J. Tildesley
- Zeeman Institute (SBIDER), School of Life Sciences, and Mathematics Institute, University of Warwick, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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37
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Prada JM, Stolk WA, Davis EL, Touloupou P, Sharma S, Muñoz J, Caja Rivera RM, Reimer LJ, Michael E, de Vlas SJ, Hollingsworth TD. Delays in lymphatic filariasis elimination programmes due to COVID-19, and possible mitigation strategies. Trans R Soc Trop Med Hyg 2021; 115:261-268. [PMID: 33515454 PMCID: PMC7928650 DOI: 10.1093/trstmh/trab004] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 12/22/2020] [Accepted: 01/11/2021] [Indexed: 12/25/2022] Open
Abstract
Background In view of the current global coronavirus disease 2019 pandemic, mass drug administration interventions for neglected tropical diseases, including lymphatic filariasis (LF), have been halted. We used mathematical modelling to estimate the impact of delaying or cancelling treatment rounds and explore possible mitigation strategies. Methods We used three established LF transmission models to simulate infection trends in settings with annual treatment rounds and programme delays in 2020 of 6, 12, 18 or 24 months. We then evaluated the impact of various mitigation strategies upon resuming activities. Results The delay in achieving the elimination goals is on average similar to the number of years the treatment rounds are missed. Enhanced interventions implemented for as little as 1 y can allow catch-up on the progress lost and, if maintained throughout the programme, can lead to acceleration of up to 3 y. Conclusions In general, a short delay in the programme does not cause a major delay in achieving the goals. Impact is strongest in high-endemicity areas. Mitigation strategies such as biannual treatment or increased coverage are key to minimizing the impact of the disruption once the programme resumes and lead to potential acceleration should these enhanced strategies be maintained.
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Affiliation(s)
- Joaquín M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Wilma A Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Headington, Oxford, UK
| | - Panayiota Touloupou
- Department of Statistics, University of Warwick, Coventry, UK.,School of Mathematics, University of Birmingham, Birmingham, UK
| | - Swarnali Sharma
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Johanna Muñoz
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rocio M Caja Rivera
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.,Center for Global Health Infectious Disease Research, University of South Florida, Tampa, FL, USA
| | - Lisa J Reimer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.,Center for Global Health Infectious Disease Research, University of South Florida, Tampa, FL, USA
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Headington, Oxford, UK
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38
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Blumberg S, Borlase A, Prada JM, Solomon AW, Emerson P, Hooper PJ, Deiner MS, Amoah B, Hollingsworth TD, Porco TC, Lietman TM. Implications of the COVID-19 pandemic in eliminating trachoma as a public health problem. Trans R Soc Trop Med Hyg 2021; 115:222-228. [PMID: 33449114 PMCID: PMC7928550 DOI: 10.1093/trstmh/traa170] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/07/2020] [Accepted: 01/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background Progress towards elimination of trachoma as a public health problem has been substantial, but the coronavirus disease 2019 (COVID-19) pandemic has disrupted community-based control efforts. Methods We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma. Results We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of 1. We find that when the basic reproduction number is <1, no significant delays in disease control will be caused. However, when the basic reproduction number is >1, significant delays can occur. In most districts, 1 y of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease. Conclusions If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.
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Affiliation(s)
- Seth Blumberg
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | | | - Joaquin M Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Anthony W Solomon
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Paul Emerson
- International Trachoma Initiative, Task Force for Global Health, Decatur, GA, USA
| | - Pamela J Hooper
- International Trachoma Initiative, Task Force for Global Health, Decatur, GA, USA
| | - Michael S Deiner
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | - Benjamin Amoah
- Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | | | - Travis C Porco
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas M Lietman
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Institute for Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA.,Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
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39
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Hollingsworth TD, Mwinzi P, Vasconcelos A, de Vlas SJ. Evaluating the potential impact of interruptions to neglected tropical disease programmes due to COVID-19. Trans R Soc Trop Med Hyg 2021; 115:201-204. [PMID: 33693894 PMCID: PMC7946803 DOI: 10.1093/trstmh/trab023] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 02/11/2021] [Indexed: 01/05/2023] Open
Affiliation(s)
- T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Pauline Mwinzi
- Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN), World Health Organization Regional Office for Africa, P.O.BOX 06 Cité du Djoué Brazzaville, Congo
| | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
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40
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Baggaley RF, Vegvari C, Dimala CA, Lipman M, Miller RF, Brown J, Degtyareva S, White HA, Hollingsworth TD, Pareek M. Health economic analyses of latent tuberculosis infection screening and preventive treatment among people living with HIV in lower tuberculosis incidence settings: a systematic review. Wellcome Open Res 2021; 6:51. [PMID: 37025515 PMCID: PMC10071141 DOI: 10.12688/wellcomeopenres.16604.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 11/20/2022] Open
Abstract
Introduction: In lower tuberculosis (TB) incidence countries (<100 cases/100,000/year), screening and preventive treatment (PT) for latent TB infection (LTBI) among people living with HIV (PLWH) is often recommended, yet guidelines advising which groups to prioritise for screening can be contradictory and implementation patchy. Evidence of LTBI screening cost-effectiveness may improve uptake and health outcomes at reasonable cost. Methods: Our systematic review assessed cost-effectiveness estimates of LTBI screening/PT strategies among PLWH in lower TB incidence countries to identify model-driving inputs and methodological differences. Databases were searched 1980-2020. Studies including health economic evaluation of LTBI screening of PLWH in lower TB incidence countries (<100 cases/100,000/year) were included. Study quality was assessed using the CHEERS checklist. Results: Of 2,644 articles screened, nine studies were included. Cost-effectiveness estimates of LTBI screening/PT for PLWH varied widely, with universal screening/PT found highly cost-effective by some studies, while only targeting to high-risk groups (such as those from mid/high TB incidence countries) deemed cost-effective by others. Cost-effectiveness of strategies screening all PLWH from studies published in the past five years varied from US$2828 to US$144,929/quality-adjusted life-year gained (2018 prices). Study quality varied, with inconsistent reporting of methods and results limiting comparability of studies. Cost-effectiveness varied markedly by screening guideline, with British HIV Association guidelines more cost-effective than NICE guidelines in the UK. Discussion: Cost-effectiveness studies of LTBI screening/PT for PLWH in lower TB incidence settings are scarce, with large variations in methods and assumptions used, target populations and screening/PT strategies evaluated. The limited evidence suggests LTBI screening/PT may be cost-effective for some PLWH groups but further research is required, particularly on strategies targeting screening/PT to PLWH at higher risk. Standardisation of model descriptions and results reporting could facilitate reliable comparisons between studies, particularly to identify those factors driving the wide disparity between cost-effectiveness estimates. Registration: PROSPERO CRD42020166338 (18/03/2020).
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Affiliation(s)
- Rebecca F. Baggaley
- Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- UCL Respiratory, University College London, London, UK
| | - Christian A. Dimala
- Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Marc Lipman
- Royal Free London National Health Service Foundation Trust, London, UK
- RUDN University, Moscow, Russian Federation
| | | | | | - Svetlana Degtyareva
- Department of Infection and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | | | | | - Manish Pareek
- Big Data Institute, University of Oxford, Oxford, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, LE1 7RH, UK
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41
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Toor J, Coffeng LE, Hamley JID, Fronterre C, Prada JM, Castaño MS, Davis EL, Godwin W, Vasconcelos A, Medley GF, Hollingsworth TD. When, Who, and How to Sample: Designing Practical Surveillance for 7 Neglected Tropical Diseases as We Approach Elimination. J Infect Dis 2021; 221:S499-S502. [PMID: 32529261 PMCID: PMC7289548 DOI: 10.1093/infdis/jiaa198] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
As neglected tropical disease programs look to consolidate the successes of moving towards elimination, we need to understand the dynamics of transmission at low prevalence to inform surveillance strategies for detecting elimination and resurgence. In this special collection, modelling insights are used to highlight drivers of local elimination, evaluate strategies for detecting resurgence, and show the importance of rational spatial sampling schemes for several neglected tropical diseases (specifically schistosomiasis, soil-transmitted helminths, lymphatic filariasis, trachoma, onchocerciasis, visceral leishmaniasis, and gambiense sleeping sickness).
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Affiliation(s)
- Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Luc E Coffeng
- Department of Public Health, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jonathan I D Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Claudio Fronterre
- Centre for Health Informatics, Computing, and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - M Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - William Godwin
- Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA
| | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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42
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Godwin W, Prada JM, Emerson P, Hooper PJ, Bakhtiari A, Deiner M, Porco TC, Mahmud H, Landskroner E, Hollingsworth TD, Medley GF, Pinsent A, Bailey R, Lietman TM, Oldenburg CE. Trachoma Prevalence After Discontinuation of Mass Azithromycin Distribution. J Infect Dis 2021; 221:S519-S524. [PMID: 32052842 PMCID: PMC7289551 DOI: 10.1093/infdis/jiz691] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background As the World Health Organization seeks to eliminate trachoma by 2020, countries are beginning to control the transmission of trachomatous inflammation–follicular (TF) and discontinue mass drug administration (MDA) with oral azithromycin. We evaluated the effect of MDA discontinuation on TF1–9 prevalence at the district level. Methods We extracted from the available data districts with an impact survey at the end of their program cycle that initiated discontinuation of MDA (TF1–9 prevalence <5%), followed by a surveillance survey conducted to determine whether TF1–9 prevalence remained below the 5% threshold, warranting discontinuation of MDA. Two independent analyses were performed, 1 regression based and 1 simulation based, that assessed the change in TF1–9 from the impact survey to the surveillance survey. Results Of the 220 districts included, TF1–9 prevalence increased to >5% from impact to surveillance survey in 9% of districts. Regression analysis indicated that impact survey TF1–9 prevalence was a significant predictor of surveillance survey TF1–9 prevalence. The proportion of simulations with >5% TF1–9 prevalence in the surveillance survey was 2%, assuming the survey was conducted 4 years after MDA. Conclusion An increase in TF1–9 prevalence may represent disease resurgence but could also be due to measurement error. Improved diagnostic tests are crucial to elimination of TF1–9 as a public health problem.
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Affiliation(s)
- William Godwin
- Francis I Proctor Foundation, University of California, San Francisco, California, USA
| | - Joaquin M Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Paul Emerson
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - P J Hooper
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Ana Bakhtiari
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Michael Deiner
- Francis I Proctor Foundation, University of California, San Francisco, California, USA.,Department of Ophthalmology, University of California, San Francisco, California, USA
| | - Travis C Porco
- Francis I Proctor Foundation, University of California, San Francisco, California, USA.,Department of Ophthalmology, University of California, San Francisco, California, USA.,Department of Epidemiology & Biostatistics, University of California, San Francisco, California, USA
| | - Hamidah Mahmud
- Francis I Proctor Foundation, University of California, San Francisco, California, USA
| | - Emma Landskroner
- Francis I Proctor Foundation, University of California, San Francisco, California, USA
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Amy Pinsent
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Robin Bailey
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Thomas M Lietman
- Francis I Proctor Foundation, University of California, San Francisco, California, USA.,Department of Ophthalmology, University of California, San Francisco, California, USA.,Department of Epidemiology & Biostatistics, University of California, San Francisco, California, USA
| | - Catherine E Oldenburg
- Francis I Proctor Foundation, University of California, San Francisco, California, USA.,Department of Ophthalmology, University of California, San Francisco, California, USA.,Department of Epidemiology & Biostatistics, University of California, San Francisco, California, USA
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43
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Toor J, Rollinson D, Turner HC, Gouvras A, King CH, Medley GF, Hollingsworth TD, Anderson RM. Achieving Elimination as a Public Health Problem for Schistosoma mansoni and S. haematobium: When Is Community-Wide Treatment Required? J Infect Dis 2021; 221:S525-S530. [PMID: 31829414 PMCID: PMC7289541 DOI: 10.1093/infdis/jiz609] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
The World Health Organization (WHO) has set elimination as a public health problem (EPHP) as a goal for schistosomiasis. As the WHO treatment guidelines for schistosomiasis are currently under revision, we investigate whether school-based or community-wide treatment strategies are required for achieving the EPHP goal. In low- to moderate-transmission settings with good school enrolment, we find that school-based treatment is sufficient for achieving EPHP. However, community-wide treatment is projected to be necessary in certain high-transmission settings as well as settings with low school enrolment. Hence, the optimal treatment strategy depends on setting-specific factors such as the species present, prevalence prior to treatment, and the age profile of infection.
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Affiliation(s)
- Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - David Rollinson
- Department of Life Sciences, Natural History Museum, London, UK.,Global Schistosomiasis Alliance, Department of Life Sciences, Natural History Museum, London, UK
| | - Hugo C Turner
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anouk Gouvras
- Global Schistosomiasis Alliance, Department of Life Sciences, Natural History Museum, London, UK
| | - Charles H King
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, USA
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,The DeWorm3 Project, Natural History Museum, London, UK
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44
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Crellen T, Sithithaworn P, Pitaksakulrat O, Khuntikeo N, Medley GF, Hollingsworth TD. Towards Evidence-based Control of Opisthorchis viverrini. Trends Parasitol 2021; 37:370-380. [PMID: 33516657 DOI: 10.1016/j.pt.2020.12.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/18/2020] [Accepted: 12/25/2020] [Indexed: 01/21/2023]
Abstract
Transmission of the carcinogenic liver fluke Opisthorchis viverrini is ongoing across Southeast Asia. Endemic countries within the region are in different stages of achieving control. However, evidence on which interventions are the most effective for reducing parasite transmission, and the resulting liver cancer, is currently lacking. Quantitative modelling can be used to evaluate different control measures against O. viverrini and assist the design of clinical trials. In this article we evaluate the epidemiological parameters that underpin models of O. viverrini and the data necessary for their estimation, with the aim of developing evidence-based strategies for parasite control at a national or regional level.
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Affiliation(s)
- Thomas Crellen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Paiboon Sithithaworn
- Department of Parasitology, Khon Kaen University, Khon Kaen 40002, Thailand; Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Opal Pitaksakulrat
- Department of Parasitology, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Narong Khuntikeo
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen 40002, Thailand; Department of Surgery, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Graham F Medley
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
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45
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Clark J, Stolk WA, Basáñez MG, Coffeng LE, Cucunubá ZM, Dixon MA, Dyson L, Hampson K, Marks M, Medley GF, Pollington TM, Prada JM, Rock KS, Salje H, Toor J, Hollingsworth TD. How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases. Gates Open Res 2021; 5:112. [PMID: 35169682 PMCID: PMC8816801 DOI: 10.12688/gatesopenres.13327.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/28/2022] [Indexed: 01/12/2023] Open
Abstract
The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals , an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.
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Affiliation(s)
- Jessica Clark
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - Zulma M. Cucunubá
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Matthew A. Dixon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- Schistosomiasis Control Initiative Foundation, London, SE11 5DP, UK
| | - Louise Dyson
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Jaspreet Toor
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
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46
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Clark J, Stolk WA, Basáñez MG, Coffeng LE, Cucunubá ZM, Dixon MA, Dyson L, Hampson K, Marks M, Medley GF, Pollington TM, Prada JM, Rock KS, Salje H, Toor J, Hollingsworth TD. How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases. Gates Open Res 2021; 5:112. [PMID: 35169682 PMCID: PMC8816801 DOI: 10.12688/gatesopenres.13327.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 01/12/2023] Open
Abstract
The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals , an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.
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Affiliation(s)
- Jessica Clark
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Wilma A. Stolk
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3000 CA, The Netherlands
| | - Zulma M. Cucunubá
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Matthew A. Dixon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- Schistosomiasis Control Initiative Foundation, London, SE11 5DP, UK
| | - Louise Dyson
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Joaquin M. Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Kat S. Rock
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Jaspreet Toor
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
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47
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Lucas TCD, Pollington TM, Davis EL, Hollingsworth TD. Responsible modelling: Unit testing for infectious disease epidemiology. Epidemics 2020; 33:100425. [PMID: 33307443 PMCID: PMC7690327 DOI: 10.1016/j.epidem.2020.100425] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/21/2020] [Accepted: 11/21/2020] [Indexed: 11/30/2022] Open
Abstract
Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.
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Affiliation(s)
- Tim C D Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. Centre for Environment and Health, School of Public Health, Imperial College, UK.
| | - Timothy M Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. MathSys CDT, University of Warwick, UK
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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48
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Toor J, Adams ER, Aliee M, Amoah B, Anderson RM, Ayabina D, Bailey R, Basáñez MG, Blok DJ, Blumberg S, Borlase A, Rivera RC, Castaño MS, Chitnis N, Coffeng LE, Crump RE, Das A, Davis CN, Davis EL, Deiner MS, Diggle PJ, Fronterre C, Giardina F, Giorgi E, Graham M, Hamley JID, Huang CI, Kura K, Lietman TM, Lucas TCD, Malizia V, Medley GF, Meeyai A, Michael E, Porco TC, Prada JM, Rock KS, Le Rutte EA, Smith ME, Spencer SEF, Stolk WA, Touloupou P, Vasconcelos A, Vegvari C, de Vlas SJ, Walker M, Hollingsworth TD. Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation. Clin Infect Dis 2020; 72:1463-1466. [PMID: 32984870 PMCID: PMC7543306 DOI: 10.1093/cid/ciaa933] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 07/10/2020] [Indexed: 11/12/2022] Open
Abstract
Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.
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Affiliation(s)
- Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Emily R Adams
- Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Maryam Aliee
- Mathematics Institute, University of Warwick, Coventry, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Benjamin Amoah
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Roy M Anderson
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom,The DeWorm3 Project, Natural History Museum, London, United Kingdom
| | - Diepreye Ayabina
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Robin Bailey
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Maria-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - David J Blok
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Seth Blumberg
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America
| | - Anna Borlase
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Rocio Caja Rivera
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - María Soledad Castaño
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Luc E Coffeng
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ronald E Crump
- Mathematics Institute, University of Warwick, Coventry, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom,The School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Aatreyee Das
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Christopher N Davis
- Mathematics Institute, University of Warwick, Coventry, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Michael S Deiner
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America,Department of Ophthalmology, University of California, San Francisco, California, United States of America
| | - Peter J Diggle
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Claudio Fronterre
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Federica Giardina
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emanuele Giorgi
- Centre for Health Informatics, Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom,Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jonathan I D Hamley
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Ching-I Huang
- Mathematics Institute, University of Warwick, Coventry, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Klodeta Kura
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Thomas M Lietman
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America,Department of Ophthalmology, University of California, San Francisco, California, United States of America,Department of Epidemiology & Biostatistics, University of California, San Francisco, California, United States of America
| | - Tim C D Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Veronica Malizia
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Aronrag Meeyai
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Edwin Michael
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Travis C Porco
- Francis I Proctor Foundation, University of California, San Francisco, California, United States of America,Department of Ophthalmology, University of California, San Francisco, California, United States of America,Department of Epidemiology & Biostatistics, University of California, San Francisco, California, United States of America
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Kat S Rock
- Mathematics Institute, University of Warwick, Coventry, United Kingdom,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Epke A Le Rutte
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands,Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Morgan E Smith
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Simon E F Spencer
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom,Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Wilma A Stolk
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
| | - Carolin Vegvari
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Sake J de Vlas
- Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martin Walker
- London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom,London Centre for Neglected Tropical Disease Research, Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, Hatfield, Hertfordshire, United Kingdom
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom,Correspondence: T. D. Hollingsworth, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford OX3 7LF, UK ()
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49
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Affiliation(s)
- Roy M Anderson
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK.
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Rebecca F Baggaley
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Rosie Maddren
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Carolin Vegvari
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
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50
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Thompson RN, Hollingsworth TD, Isham V, Arribas-Bel D, Ashby B, Britton T, Challenor P, Chappell LHK, Clapham H, Cunniffe NJ, Dawid AP, Donnelly CA, Eggo RM, Funk S, Gilbert N, Glendinning P, Gog JR, Hart WS, Heesterbeek H, House T, Keeling M, Kiss IZ, Kretzschmar ME, Lloyd AL, McBryde ES, McCaw JM, McKinley TJ, Miller JC, Morris M, O'Neill PD, Parag KV, Pearson CAB, Pellis L, Pulliam JRC, Ross JV, Tomba GS, Silverman BW, Struchiner CJ, Tildesley MJ, Trapman P, Webb CR, Mollison D, Restif O. Key questions for modelling COVID-19 exit strategies. Proc Biol Sci 2020; 287:20201405. [PMID: 32781946 PMCID: PMC7575516 DOI: 10.1098/rspb.2020.1405] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/21/2020] [Indexed: 12/15/2022] Open
Abstract
Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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Affiliation(s)
- Robin N. Thompson
- Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
- Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | | | - Valerie Isham
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
| | - Daniel Arribas-Bel
- School of Environmental Sciences, University of Liverpool, Brownlow Street, Liverpool L3 5DA, UK
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | - Ben Ashby
- Department of Mathematical Sciences, University of Bath, North Road, Bath BA2 7AY, UK
| | - Tom Britton
- Department of Mathematics, Stockholm University, Kräftriket, 106 91 Stockholm, Sweden
| | - Peter Challenor
- College of Engineering, Mathematical and Physical Sciences, University of Exeter, Exeter EX4 4QE, UK
| | - Lauren H. K. Chappell
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK
| | - Hannah Clapham
- Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive, Singapore117549, Singapore
| | - Nik J. Cunniffe
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - A. Philip Dawid
- Statistical Laboratory, University of Cambridge, Wilberforce Road, Cambridge CB3 0WB, UK
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial CollegeLondon, Norfolk Place, London W2 1PG, UK
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Nigel Gilbert
- Department of Sociology, University of Surrey, Stag Hill, Guildford GU2 7XH, UK
| | - Paul Glendinning
- Department of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Julia R. Gog
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - William S. Hart
- Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Yalelaan, 3584 CL Utrecht, The Netherlands
| | - Thomas House
- IBM Research, The Hartree Centre, Daresbury, Warrington WA4 4AD, UK
- Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Matt Keeling
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - István Z. Kiss
- School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton BN1 9QH, UK
| | - Mirjam E. Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Alun L. Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA
| | - Emma S. McBryde
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland 4811, Australia
| | - James M. McCaw
- School of Mathematics and Statistics, University of Melbourne, Carlton, Victoria 3010, Australia
| | - Trevelyan J. McKinley
- College of Medicine and Health, University of Exeter, Barrack Road, Exeter EX2 5DW, UK
| | - Joel C. Miller
- Department of Mathematics and Statistics, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Martina Morris
- Department of Sociology, University of Washington, Savery Hall, Seattle, WA 98195, USA
| | - Philip D. O'Neill
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial CollegeLondon, Norfolk Place, London W2 1PG, UK
| | - Carl A. B. Pearson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Jonkershoek Road, Stellenbosch 7600, South Africa
| | - Lorenzo Pellis
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Juliet R. C. Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Jonkershoek Road, Stellenbosch 7600, South Africa
| | - Joshua V. Ross
- School of Mathematical Sciences, University of Adelaide, South Australia 5005, Australia
| | | | - Bernard W. Silverman
- Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, UK
- Rights Lab, University of Nottingham, Highfield House, Nottingham NG7 2RD, UK
| | - Claudio J. Struchiner
- Escola de Matemática Aplicada, Fundação Getúlio Vargas, Praia de Botafogo, 190 Rio de Janeiro, Brazil
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Pieter Trapman
- Department of Mathematics, Stockholm University, Kräftriket, 106 91 Stockholm, Sweden
| | - Cerian R. Webb
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, UK
| | - Olivier Restif
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
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