1
|
Gunderson AK, Recalde-Coronel C, Zaitchick BF, Yori PP, Rengifo Pinedo S, Paredes Olortegui M, Kosek M, Vinetz JM, Pan WK. A prospective cohort study linking migration, climate, and malaria risk in the Peruvian Amazon. Epidemiol Infect 2023; 151:e202. [PMID: 38031496 PMCID: PMC10753477 DOI: 10.1017/s0950268823001838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/02/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
Migration is an important risk factor for malaria transmission for malaria transmission, creating networks that connect Plasmodium between communities. This study aims to understand the timing of why people in the Peruvian Amazon migrated and how characteristics of these migrants are associated with malaria risk. A cohort of 2,202 participants was followed for three years (July 2006 - October 2009), with thrice-weekly active surveillance to record infection and recent travel, which included travel destination(s) and duration away. Migration occurred more frequently in the dry season, but the 7-day rolling mean (7DRM) streamflow was positively correlated with migration events (OR 1.25 (95% CI: 1.138, 1.368)). High-frequency and low-frequency migrant populations reported 9.7 (IRR 7.59 (95% CI:.381, 13.160)) and 4.1 (IRR 2.89 (95% CI: 1.636, 5.099)) times more P. vivax cases than those considered non-migrants and 30.7 (IRR 32.42 (95% CI: 7.977, 131.765)) and 7.4 (IRR 7.44 (95% CI: 1.783, 31.066)) times more P. falciparum cases, respectively. High-frequency migrants employed in manual labour within their community were at 2.45 (95% CI: 1.113, 5.416) times higher risk than non-employed low-frequency migrants. This study confirms the importance of migration for malaria risk as well as factors increasing risk among the migratory community, including, sex, occupation, and educational status.
Collapse
Affiliation(s)
- Annika K. Gunderson
- Department of Epidemiology, Gilling School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Cristina Recalde-Coronel
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
- Facultad de Ingeniería Marítima y Ciencias del Mar, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - Benjamin F. Zaitchick
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Pablo Peñataro Yori
- Asociación Benéfica Prisma, Iquitos, Peru
- Division of Infectious Diseases, University of Virginia, Charlottesville, Virginia, USA
| | | | | | - Margaret Kosek
- Asociación Benéfica Prisma, Iquitos, Peru
- Division of Infectious Diseases, University of Virginia, Charlottesville, Virginia, USA
| | - Joseph M. Vinetz
- Section of Infectious Diseases, Department of Internal Medicine, School of Medicine, Yale University, New Haven, USA
- International Centers of Excellence for Malaria Research – Amazonia, Laboratorio de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- VA Connecticut Healthcare System, West Haven, CT, USA
- Institute of Tropical Medicine Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - William K. Pan
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Nicholas School of the Environment, Duke University, Durham, NC, USA
| |
Collapse
|
2
|
Tatem AJ. Small area population denominators for improved disease surveillance and response. Epidemics 2022; 41:100641. [PMID: 36228440 PMCID: PMC9534780 DOI: 10.1016/j.epidem.2022.100641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/12/2022] [Accepted: 10/04/2022] [Indexed: 12/29/2022] Open
Abstract
The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.
Collapse
Affiliation(s)
- A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| |
Collapse
|
3
|
The influence of cross-regional medical treatment on total medical expenses, medical insurance payments, and out-of-pocket expenses of patients with malignant tumors in Chinese low-income areas. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2022; 20:35. [PMID: 35864496 PMCID: PMC9306213 DOI: 10.1186/s12962-022-00368-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND In recent years, due to the increasing number of cross-regional medical patients, countries around the world have issued a series of policies or regulations to reduce their out-of-pocket burden. In this context, this study intended to explore the impact of the Spatio-temporal characteristics of cross-regional medical treatment on total medical expenses, medical insurance payments, and out-of-pocket expenses of patients with malignant tumors in low-income areas. METHODS This study included 54,904 data of cross-provincial medical treatment of malignant tumor patients insured in Heilongjiang Province, China in 2020. Firstly, Microsoft Excel 2019 and ArcGIS 10.2 were applied to conduct a descriptive analysis of the Spatio-temporal characteristics of their cross-provincial medical treatment. Then, binary and multivariate logistic regression models were used to explore the specific impact of economic level and geographical distance of medical regions on total medical expenses, medical insurance payments, and out-of-pocket expenses. RESULTS The number of cross-regional medical patients showed a gradual upward trend from February to December, and fell back in January. They were concentrated in regions with high economic level and short distance from the insured region, where were more likely to form the group with high out-of-pocket expenses (AOR = 3.620, P < 0.001; AOR = 1.882, P < 0.001). While this possibility in middle-distance medical regions were less (AOR = 0.545, P < 0.001). Afterwards, two sensitivity analysis methods showed that the results were robust. CONCLUSION The number of cross-regional medical patients with malignant tumors in low-income areas is affected by seasonal factors, meanwhile, their total medical expenses, actual medical insurance payment levels, and out-of-pocket expenses are all affected by the economic level and geographical distance of medical regions. And the middle-distance medical regions may be the best choice for patients with planned cross-regional medical treatment. These provide some evidence for policymakers to improve the fairness and sustainability of medical security for cross-regional medical patients and reduce their direct economic burden of disease.
Collapse
|
4
|
Tatem AJ. Small area population denominators for improved disease surveillance and response. Epidemics 2022; 40:100597. [PMID: 35749928 PMCID: PMC9212890 DOI: 10.1016/j.epidem.2022.100597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022] Open
Abstract
The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.
Collapse
Affiliation(s)
- A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| |
Collapse
|
5
|
Lai S, Sorichetta A, Steele J, Ruktanonchai CW, Cunningham AD, Rogers G, Koper P, Woods D, Bondarenko M, Ruktanonchai NW, Shi W, Tatem AJ. Global holiday datasets for understanding seasonal human mobility and population dynamics. Sci Data 2022; 9:17. [PMID: 35058466 PMCID: PMC8776767 DOI: 10.1038/s41597-022-01120-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 12/10/2021] [Indexed: 11/17/2022] Open
Abstract
Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.
Collapse
Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Jessica Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Corrine W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Alexander D Cunningham
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Grant Rogers
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Patrycja Koper
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Dorothea Woods
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Weifeng Shi
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| |
Collapse
|
6
|
Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
Collapse
Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| |
Collapse
|
7
|
Milusheva S. Managing the spread of disease with mobile phone data. JOURNAL OF DEVELOPMENT ECONOMICS 2020; 147:102559. [PMID: 33144750 PMCID: PMC7561616 DOI: 10.1016/j.jdeveco.2020.102559] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/19/2020] [Accepted: 08/20/2020] [Indexed: 06/04/2023]
Abstract
While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.66 additional cases reported in the health facility at the traveler's destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.
Collapse
|
8
|
Carrasco-Escobar G, Fornace K, Wong D, Padilla-Huamantinco PG, Saldaña-Lopez JA, Castillo-Meza OE, Caballero-Andrade AE, Manrique E, Ruiz-Cabrejos J, Barboza JL, Rodriguez H, Henostroza G, Gamboa D, Castro MC, Vinetz JM, Llanos-Cuentas A. Open-Source 3D Printable GPS Tracker to Characterize the Role of Human Population Movement on Malaria Epidemiology in River Networks: A Proof-of-Concept Study in the Peruvian Amazon. Front Public Health 2020; 8:526468. [PMID: 33072692 PMCID: PMC7542225 DOI: 10.3389/fpubh.2020.526468] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 08/21/2020] [Indexed: 11/13/2022] Open
Abstract
Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.
Collapse
Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Kimberly Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Daniel Wong
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Pierre G Padilla-Huamantinco
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose A Saldaña-Lopez
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Ober E Castillo-Meza
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Armando E Caballero-Andrade
- Departamento de Ingenieria, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.,Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose Luis Barboza
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - German Henostroza
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dionicia Gamboa
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Joseph M Vinetz
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, United States
| | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru
| |
Collapse
|
9
|
Jones JH, Hazel A, Almquist Z. Transmission-dynamics models for the SARS Coronavirus-2. Am J Hum Biol 2020; 32:e23512. [PMID: 32978876 PMCID: PMC7536961 DOI: 10.1002/ajhb.23512] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/02/2020] [Accepted: 08/03/2020] [Indexed: 12/22/2022] Open
Affiliation(s)
| | - Ashley Hazel
- Department of Earth System ScienceStanford UniversityStanfordCaliforniaUSA
| | - Zack Almquist
- Department of SociologyUniversity of WashingtonSeattleWashingtonUSA
| |
Collapse
|
10
|
Araújo MB, Mestre F, Naimi B. Ecological and epidemiological models are both useful for SARS-CoV-2. Nat Ecol Evol 2020; 4:1153-1154. [DOI: 10.1038/s41559-020-1246-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
11
|
Cutts FT, Dansereau E, Ferrari MJ, Hanson M, McCarthy KA, Metcalf CJE, Takahashi S, Tatem AJ, Thakkar N, Truelove S, Utazi E, Wesolowski A, Winter AK. Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications. Vaccine 2020; 38:979-992. [PMID: 31787412 PMCID: PMC6996156 DOI: 10.1016/j.vaccine.2019.11.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 01/30/2023]
Abstract
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
Collapse
Affiliation(s)
- F T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - E Dansereau
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - M J Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - M Hanson
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - K A McCarthy
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - S Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - N Thakkar
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - S Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - E Utazi
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - A Wesolowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - A K Winter
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
12
|
Mahikul W, White LJ, Poovorawan K, Soonthornworasiri N, Sukontamarn P, Chanthavilay P, Medley GF, Pan-ngum W. Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis. PLoS Negl Trop Dis 2019; 13:e0007380. [PMID: 31071094 PMCID: PMC6529009 DOI: 10.1371/journal.pntd.0007380] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 05/21/2019] [Accepted: 04/10/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Melioidosis is an infectious disease that is transmitted mainly through contact with contaminated soil or water, and exhibits marked seasonality in most settings, including Southeast Asia. In this study, we used mathematical modelling to examine the impacts of such demographic changes on melioidosis incidence, and to predict the disease burden in a developing country such as Thailand. METHODOLOGY/PRINCIPAL FINDINGS A melioidosis infection model was constructed which included demographic data, diabetes mellitus (DM) prevalence, and melioidosis disease processes. The model was fitted to reported melioidosis incidence in Thailand by age, sex, and geographical area, between 2008 and 2015, using a Bayesian Markov Chain Monte Carlo (MCMC) approach. The model was then used to predict the disease burden and future trends of melioidosis incidence in Thailand. Our model predicted two-fold higher incidence rates of melioidosis compared with national surveillance data from 2015. The estimated incidence rates among males were two-fold greater than those in females. Furthermore, the melioidosis incidence rates in the Northeast region population, and among the transient population, were more than double compared to the non-Northeast region population. The highest incidence rates occurred in males aged 45-59 years old for all regions. The average incidence rate of melioidosis between 2005 and 2035 was predicted to be 11.42 to 12.78 per 100,000 population per year, with a slightly increasing trend. Overall, it was estimated that about half of all cases of melioidosis were symptomatic. In addition, the model suggested a greater susceptibility to melioidosis in diabetic compared with non-diabetic individuals. CONCLUSIONS/SIGNIFICANCE The increasing trend of melioidosis incidence rates was significantly higher among working-age Northeast and transient populations, males aged ≥45 years old, and diabetic individuals. Targeted intervention strategies, such as health education and awareness raising initiatives, should be implemented on high-risk groups, such as those living in the Northeast region, and the seasonally transient population.
Collapse
Affiliation(s)
- Wiriya Mahikul
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Lisa J. White
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Kittiyod Poovorawan
- Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | | | | | - Phetsavanh Chanthavilay
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Institute of Research and Education Development, UHS, Vientiane, Lao PDR
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease & Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Wirichada Pan-ngum
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| |
Collapse
|
13
|
Marinho de Souza J, Giuffrida R, Ramos APM, Morceli G, Coelho CH, Pimenta Rodrigues MV. Mother-to-child transmission and gestational syphilis: Spatial-temporal epidemiology and demographics in a Brazilian region. PLoS Negl Trop Dis 2019; 13:e0007122. [PMID: 30789909 PMCID: PMC6383870 DOI: 10.1371/journal.pntd.0007122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 01/03/2019] [Indexed: 11/29/2022] Open
Abstract
Syphilis is a Sexually Transmitted Infection (IST) with significant importance to public health, due to its impact during pregnancy (Gestational Syphilis-GS); especially because syphilis can affect fetus and neonates' development (mother-to-child transmission-MTCT of syphilis), by increasing susceptibility to abortion, premature birth, skeletal malformations, meningitis and pneumonia. Measures to control and eliminate MTCT of syphilis have failed on the last few years in Brazil and this research aimed to identify the seasonality of notified cases of syphilis in a region of São Paulo state. The studied region, Pontal do Paranapanema, comprises 32 cities located in the West of São Paulo state, in Brazil. Data collected from the National System of Aggravations and Notification (SINAN) website was used to calculate the incidence rate of GS and MTCT. The incidence rate of GS was acquired dividing number of cases by number of women in each municipality and MTCT using number of live births in each year (from 2007 to 2013) in each municipality. This result was then, standardized multiplying incidence rate by 10,000 and expressed as incidence/10,000 women or live births, for GS and MTCT, respectively. To identify possible endemic/epidemic periods, a control diagram was performed using the standard deviation (SD) of incidence rate. Thematic maps representing the spatial distribution of incidence rates were constructed using a Geographic Information System software (GIS, based on cartographic vector available on the Brazilian Institute of Geography and Statistics (IBGE) website. Eighty cases of GS and 61 cases of MTCT were notified in the studied region. An increase of GS notification was detected in the Pontal do Paranapanema in 2011 followed by an increase in number of MTCT cases in the subsequent year, suggesting inefficacy in the treatment during gestational period. Most of those cases were reported on February and November which suggested seasonality for this IST in the region. The control diagram, based on the inputs collected from SINAN, showed no endemic period; however, the most susceptible month to happen an endemic event of GS and MTCT was February. Our study provided a new methodology to understand the syphilis dynamics as a potential tool to improve the success of future measures to control and possibly eliminate MTCT of syphilis.
Collapse
Affiliation(s)
- Joyce Marinho de Souza
- Faculdade de Ciências da Saúde, Biomedicina, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
- Laboratório de Biologia Molecular de Microrganismos, Universidade Estadual Paulista, Londrina, PR, Brazil
| | - Rogério Giuffrida
- Programa de pós-graduação em Meio Ambiente e Desenvolvimento Regional, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
- Programa de pós-graduação em Ciência Animal, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
| | - Ana Paula Marques Ramos
- Programa de pós-graduação em Meio Ambiente e Desenvolvimento Regional, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
| | - Glilciane Morceli
- Faculdade de Ciências da Saúde, Biomedicina, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
- Mestrado em Ciências da Saúde, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
| | - Camila H. Coelho
- Laboratory of Malaria Immunology and Vaccinology, DIR, NIAID, NIH, Bethesda, MD, United States of America
| | - Marcus Vinícius Pimenta Rodrigues
- Faculdade de Ciências da Saúde, Biomedicina, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
- Programa de pós-graduação em Meio Ambiente e Desenvolvimento Regional, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
- Mestrado em Ciências da Saúde, Universidade do Oeste Paulista, Presidente Prudente, SP, Brazil
| |
Collapse
|
14
|
Carrasco-Escobar G, Castro MC, Barboza JL, Ruiz-Cabrejos J, Llanos-Cuentas A, Vinetz JM, Gamboa D. Use of open mobile mapping tool to assess human mobility traceability in rural offline populations with contrasting malaria dynamics. PeerJ 2019; 7:e6298. [PMID: 30697487 PMCID: PMC6346981 DOI: 10.7717/peerj.6298] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 12/18/2018] [Indexed: 11/20/2022] Open
Abstract
Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.
Collapse
Affiliation(s)
- Gabriel Carrasco-Escobar
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Jose Luis Barboza
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Joseph M Vinetz
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Department of Infectious diseases, School of Medicine, Yale University, New Haven, CT, United States of America
| | - Dionicia Gamboa
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru.,Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.,Departamento de Ciencias Celulares y Moleculares, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| |
Collapse
|
15
|
Li L, Li J, Jiang Z, Zhao L, Zhao P. Methods of Population Spatialization Based on the Classification Information of Buildings from China's First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China. SENSORS 2018; 18:s18082558. [PMID: 30081569 PMCID: PMC6111606 DOI: 10.3390/s18082558] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 07/25/2018] [Accepted: 08/03/2018] [Indexed: 11/16/2022]
Abstract
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.
Collapse
Affiliation(s)
- Linze Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Jiansong Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Zilong Jiang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Lingli Zhao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
- State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430072, China.
| | - Pengcheng Zhao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| |
Collapse
|
16
|
The extensive networks of frequent population mobility in the Samoan Islands and their implications for infectious disease transmission. Sci Rep 2018; 8:10136. [PMID: 29973612 PMCID: PMC6031642 DOI: 10.1038/s41598-018-28081-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 06/15/2018] [Indexed: 11/09/2022] Open
Abstract
Population mobility has been demonstrated to contribute to the persistent transmission and global diffusion of epidemics. In the Pacific Islands, population mobility is particularly important for emerging infectious diseases, disease elimination programs, and diseases spread by close contact. The extent of population mobility between American Samoa villages, Samoa districts and other countries was investigated based on travel data collected during community surveys in American Samoa in 2010 and 2014. Within American Samoa, workers commuted daily across the whole of the main island of Tutuila, with work hubs drawing from villages across the island. Of the 670 adult workers surveyed, 37% had traveled overseas in the past year, with 68% of trips to Samoa. Of children aged 8–13 years (n = 337), 57% had traveled overseas, with 55% of trips to Samoa. An extensive network of connections between American Samoa villages and Samoa districts was demonstrated, with most trips lasting one week to one month. Our study showed that populations in the Samoan islands are highly mobile, and quantified the extent and destinations of their travels. Our findings offer insight into the impact of population mobility on the transmission of infectious diseases and data to refine existing models of disease transmission in the Pacific islands.
Collapse
|
17
|
Booth M, Clements A. Neglected Tropical Disease Control - The Case for Adaptive, Location-specific Solutions. Trends Parasitol 2018; 34:272-282. [PMID: 29500033 DOI: 10.1016/j.pt.2018.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 01/31/2018] [Accepted: 02/01/2018] [Indexed: 02/07/2023]
Abstract
The world is experiencing environmental and social change at an unprecedented rate, with the effects being felt at local, regional, and international scales. This phenomenon may disrupt interventions against neglected tropical diseases (NTDs) that operate on the basis of linear scaling and 'one-size-fits-all'. Here we argue that investment in field-based data collection and building modelling capacity is required; that it is important to consider unintended consequences of interventions; that inferences can be drawn from wildlife ecology; and that interventions should become more location-specific. Collectively, these ideas underpin the development of adaptive decision-support tools that are sufficiently flexible to address emerging issues within the Anthropocene.
Collapse
Affiliation(s)
- Mark Booth
- Faculty of Medical Sciences, Newcastle University, UK.
| | - Archie Clements
- Research School of Population Health, Australian National University, Australia
| |
Collapse
|
18
|
Buckee CO, Cardenas MIE, Corpuz J, Ghosh A, Haque F, Karim J, Mahmud AS, Maude RJ, Mensah K, Motaze NV, Nabaggala M, Metcalf CJE, Mioramalala SA, Mubiru F, Peak CM, Pramanik S, Rakotondramanga JM, Remera E, Sinha I, Sovannaroth S, Tatem AJ, Zaw W. Productive disruption: opportunities and challenges for innovation in infectious disease surveillance. BMJ Glob Health 2018. [PMID: 29527343 PMCID: PMC5841510 DOI: 10.1136/bmjgh-2017-000538] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Maria I E Cardenas
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - June Corpuz
- Epidemiology Bureau, Department of Health of the Philippines, Manila, Philippines
| | - Arpita Ghosh
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | - Farhana Haque
- Programme for Emerging Infections (PEI), Infectious Diseases Division (IDD), ICDDR, B, Dhaka, Bangladesh
| | - Jahirul Karim
- Disease Control Department, Directorate General of Health Services, Dhaka, Bangladesh.,Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Ayesha S Mahmud
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Richard J Maude
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA.,Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Keitly Mensah
- Dept of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Maria Nabaggala
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | | | | | - Frank Mubiru
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | - Corey M Peak
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Santanu Pramanik
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | | | | | - Ipsita Sinha
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Siv Sovannaroth
- Technical Bureau, National Malaria Control Program, Pnom Penh, Cambodia
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - Win Zaw
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| |
Collapse
|
19
|
Buckee CO, Cardenas MIE, Corpuz J, Ghosh A, Haque F, Karim J, Mahmud AS, Maude RJ, Mensah K, Motaze NV, Nabaggala M, Metcalf CJE, Mioramalala SA, Mubiru F, Peak CM, Pramanik S, Rakotondramanga JM, Remera E, Sinha I, Sovannaroth S, Tatem AJ, Zaw W. Productive disruption: opportunities and challenges for innovation in infectious disease surveillance. BMJ Glob Health 2018; 3:e000538. [PMID: 29527343 DOI: 10.1136/bmjgh-2017-00053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 11/16/2017] [Accepted: 11/17/2017] [Indexed: 05/27/2023] Open
Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Maria I E Cardenas
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - June Corpuz
- Epidemiology Bureau, Department of Health of the Philippines, Manila, Philippines
| | - Arpita Ghosh
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | - Farhana Haque
- Programme for Emerging Infections (PEI), Infectious Diseases Division (IDD), ICDDR, B, Dhaka, Bangladesh
| | - Jahirul Karim
- Disease Control Department, Directorate General of Health Services, Dhaka, Bangladesh
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Ayesha S Mahmud
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Richard J Maude
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Keitly Mensah
- Dept of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Maria Nabaggala
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | | | | | - Frank Mubiru
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | - Corey M Peak
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Santanu Pramanik
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | | | | | - Ipsita Sinha
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Siv Sovannaroth
- Technical Bureau, National Malaria Control Program, Pnom Penh, Cambodia
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - Win Zaw
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| |
Collapse
|
20
|
Wesolowski A, Zu Erbach-Schoenberg E, Tatem AJ, Lourenço C, Viboud C, Charu V, Eagle N, Engø-Monsen K, Qureshi T, Buckee CO, Metcalf CJE. Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics. Nat Commun 2017; 8:2069. [PMID: 29234011 PMCID: PMC5727034 DOI: 10.1038/s41467-017-02064-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 11/03/2017] [Indexed: 11/08/2022] Open
Abstract
Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.
Collapse
Affiliation(s)
- Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.
| | - Elisabeth Zu Erbach-Schoenberg
- WorldPop, Department of Geography, University of Southampton, University Road, Southampton, SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, SE-11355, Stockholm, Sweden
| | - Andrew J Tatem
- WorldPop, Department of Geography, University of Southampton, University Road, Southampton, SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, SE-11355, Stockholm, Sweden
| | - Christopher Lourenço
- WorldPop, Department of Geography, University of Southampton, University Road, Southampton, SO17 1BJ, UK
- Clinton Health Access Initiative, 383 Dorchester Avenue Suite 400, Boston, MA, 02127, USA
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, 31 Center Drive, Bethesda, MD, 20892, USA
| | - Vivek Charu
- Fogarty International Center, National Institutes of Health, 31 Center Drive, Bethesda, MD, 20892, USA
| | - Nathan Eagle
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | | | - Taimur Qureshi
- Telenor Research, Snarøyveien 30, N-1360, Fornebu, Norway
| | - Caroline O Buckee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, 106A Guyot Lane, Princeton, NJ, 08544, USA
- Woodrow Wilson School, Princeton University, Robertson Hall, Princeton, NJ, 08544, USA
| |
Collapse
|
21
|
Gibb R, Moses LM, Redding DW, Jones KE. Understanding the cryptic nature of Lassa fever in West Africa. Pathog Glob Health 2017; 111:276-288. [PMID: 28875769 PMCID: PMC5694855 DOI: 10.1080/20477724.2017.1369643] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Lassa fever (LF) is increasingly recognized by global health institutions as an important rodent-borne disease with severe impacts on some of West Africa's poorest communities. However, our knowledge of LF ecology, epidemiology and distribution is limited, which presents barriers to both short-term disease forecasting and prediction of long-term impacts of environmental change on Lassa virus (LASV) zoonotic transmission dynamics. Here, we synthesize current knowledge to show that extrapolations from past research have produced an incomplete picture of the incidence and distribution of LF, with negative consequences for policy planning, medical treatment and management interventions. Although the recent increase in LF case reports is likely due to improved surveillance, recent studies suggest that future socio-ecological changes in West Africa may drive increases in LF burden. Future research should focus on the geographical distribution and disease burden of LF, in order to improve its integration into public policy and disease control strategies.
Collapse
Affiliation(s)
- Rory Gibb
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Lina M. Moses
- Department of Global Community Health and Behavioral Sciences, Tulane University, New Orleans, LA, USA
| | - David W. Redding
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Kate E. Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK
- Institute of Zoology, Zoological Society of London, London, UK
| |
Collapse
|