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Yang X, Wang H, Lu H. Hotspots and Trends in Research on Early Warning of Infectious Diseases: A Bibliometric Analysis Using CiteSpace. Healthcare (Basel) 2025; 13:1293. [PMID: 40508906 PMCID: PMC12155305 DOI: 10.3390/healthcare13111293] [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: 04/22/2025] [Revised: 05/24/2025] [Accepted: 05/26/2025] [Indexed: 06/16/2025] Open
Abstract
Background: Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics appear to be more frequent, complex, and harder to prevent. Early warning systems can significantly reduce outbreak response times, contributing to better patient outcomes. Improving early warning systems and methods might be one of the most effective responses. This study employs a bibliometric analysis to dissect the global research hotspots and evolutionary trends in the field of infectious disease early warning, with the aim of providing guidance for optimizing public health emergency management strategies. Methods: Publications related to the role of early warning systems in detecting and responding to infectious disease outbreaks from 1999 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace software was used to analyze the datasets and generate knowledge visualization maps. Results: A total of 798 relevant publications are included. The number of annual publications has sharply increased since 2000. The USA produced the highest number of publications and established the most extensive cooperation relationships. The Chinese Center for Disease Control & Prevention was the most productive institution. Drake, John M was the most prolific author, while the World Health Organization and AHMED W were the most cited authors. The top two cited references mainly focused on wastewater surveillance of SARS-CoV-2. The most common keywords were "infectious disease", "outbreak", "transmission", "virus", and "climate change". The basic keyword "climate" ranked the first and long duration with the strongest citation burst. "SARS-CoV-2", "One Health", "early warning system", "artificial intelligence (AI)", and "wastewater-based epidemiology (WBE)" were emerging research foci. Conclusions: Over the past two decades, research on early warning of infectious diseases has focused on climate change, influenza, SARS, virus, machine learning, warning signals and systems, artificial intelligence, and so on. Current research hotspots include wastewater-based epidemiology, sewage, One Health, and artificial intelligence, as well as the early warning and monitoring of COVID-19. Research foci in this area have evolved from focusing on climate-disease interactions to pathogen monitoring systems, and ultimately to the "One Health" integrated framework. Our research findings underscore the imperative for public health policymakers to prioritize investments in real-time surveillance infrastructure, particularly wastewater-based epidemiology and AI-driven predictive models, and strengthen interdisciplinary collaboration frameworks under the One Health paradigm. Developing an integrated human-animal-environment monitoring system will serve as a critical development direction for early warning systems for epidemics.
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Affiliation(s)
| | | | - Hui Lu
- Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China; (X.Y.); (H.W.)
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2
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Hamilton KA, Harrison JC, Mitchell J, Weir M, Verhougstraete M, Haas CN, Nejadhashemi AP, Libarkin J, Aw TG, Bibby K, Bivins A, Brown J, Dean K, Dunbar G, Eisenberg J, Emelko M, Gerrity D, Gurian PL, Hartnett E, Jahne M, Jones RM, Julian TR, Li H, Li Y, Gibson JM, Medema G, Meschke JS, Mraz A, Murphy H, Oryang D, Johnson Owusu-Ansah EDG, Pasek E, Pradhan AK, Pepe Razzolini MT, Ryan MO, Schoen M, Smeets PWMH, Sollera J, Solo-Gabriele H, Williams C, Wilson AM, Zimmer-Faust A, Alja’fari J, Rose JB. Research gaps and priorities for quantitative microbial risk assessment (QMRA). RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:2521-2536. [PMID: 38772724 PMCID: PMC11560611 DOI: 10.1111/risa.14318] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 03/12/2024] [Accepted: 04/28/2024] [Indexed: 05/23/2024]
Abstract
The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.
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Affiliation(s)
- Kerry A. Hamilton
- The Biodesign Institute Center for Environmental Health Engineering, Arizona State University, 1001 S. McAllister Ave, Tempe AZ 85281
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe AZ 85281
| | - Joanna Ciol Harrison
- The Biodesign Institute Center for Environmental Health Engineering, Arizona State University, 1001 S. McAllister Ave, Tempe AZ 85281
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe AZ 85281
| | - Jade Mitchell
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
| | - Mark Weir
- Division of Environmental Health Sciences and Sustainability Institute, The Ohio State University, Columbus, OH 43210
| | - Marc Verhougstraete
- Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona 85724
| | - Charles N. Haas
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA
| | - A. Pouyan Nejadhashemi
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
| | - Julie Libarkin
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI
| | - Tiong Gim Aw
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112
| | - Kyle Bibby
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, IN 46556, USA
| | - Aaron Bivins
- Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Joe Brown
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kara Dean
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
| | - Gwyneth Dunbar
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
| | - Joseph Eisenberg
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor MI 48103, USA
| | - Monica Emelko
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 5H1, Canada
| | - Daniel Gerrity
- Applied Research and Development Center, Southern Nevada Water Authority, Las Vegas, NV 89193
| | - Patrick L. Gurian
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA
| | | | - Michael Jahne
- Office of Research and Development, United States Environmental Protection Agency, 26 W Martin Luther King Dr, Cincinnati, OH, USA 45268
| | - Rachael M. Jones
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, 650 S Charles E Young Dr. S., Los Angeles CA 90095, USA
| | - Timothy R. Julian
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Hongwan Li
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
| | - Yanbin Li
- Department of Biological and Agricultural Engineering, The University of Arkansas, Fayetteville, AR 72701
| | - Jacqueline MacDonald Gibson
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695
| | - Gertjan Medema
- KWR Water Research Institute, The Netherlands
- TU Delft, The Netherlands
| | - J. Scott Meschke
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, 4225 Roosevelt Way, suite 100, Seattle, WA 98105-6099
| | - Alexis Mraz
- Department of Public Health, School of Nursing, Health and Exercise Science, The College of New Jersey, 2000 Pennington Ave, Ewing, NJ 08618
| | | | - David Oryang
- Center for Food Safety and Applied Nutrition (CFSAN), US Food and Drug Administration (USFDA)
| | | | - Emily Pasek
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI
| | - Abani K. Pradhan
- Department of Nutrition and Food Science & Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA
| | | | - Michael O. Ryan
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA
| | - Mary Schoen
- Soller Environmental, LLC, 3022 King St Berkeley, CA 94703, USA
| | | | - Jeffrey Sollera
- Division of Environmental Health Sciences and Sustainability Institute, The Ohio State University, Columbus, OH 43210
| | - Helena Solo-Gabriele
- Department of Chemical, Environmental, and Materials Engineering, College of Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA
| | - Clinton Williams
- US Arid Land Agricultural Research Center, USDA-ARS, 21881 N cardon Ln, Maricopa, AZ 85138, USA
| | - Amanda Marie Wilson
- Community, Environment & Policy Department, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona
| | - Amy Zimmer-Faust
- Southern California Coastal Water Research Project, Costa Mesa, California, USA 92626
| | - Jumana Alja’fari
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, MD 20899
| | - Joan B. Rose
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI
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3
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Asplin P, Mancy R, Finnie T, Cumming F, Keeling MJ, Hill EM. Symptom propagation in respiratory pathogens of public health concern: a review of the evidence. J R Soc Interface 2024; 21:20240009. [PMID: 39045688 PMCID: PMC11267474 DOI: 10.1098/rsif.2024.0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 05/28/2024] [Indexed: 07/25/2024] Open
Abstract
Symptom propagation occurs when the symptom set an individual experiences is correlated with the symptom set of the individual who infected them. Symptom propagation may dramatically affect epidemiological outcomes, potentially causing clusters of severe disease. Conversely, it could result in chains of mild infection, generating widespread immunity with minimal cost to public health. Despite accumulating evidence that symptom propagation occurs for many respiratory pathogens, the underlying mechanisms are not well understood. Here, we conducted a scoping literature review for 14 respiratory pathogens to ascertain the extent of evidence for symptom propagation by two mechanisms: dose-severity relationships and route-severity relationships. We identify considerable heterogeneity between pathogens in the relative importance of the two mechanisms, highlighting the importance of pathogen-specific investigations. For almost all pathogens, including influenza and SARS-CoV-2, we found support for at least one of the two mechanisms. For some pathogens, including influenza, we found convincing evidence that both mechanisms contribute to symptom propagation. Furthermore, infectious disease models traditionally do not include symptom propagation. We summarize the present state of modelling advancements to address the methodological gap. We then investigate a simplified disease outbreak scenario, finding that under strong symptom propagation, isolating mildly infected individuals can have negative epidemiological implications.
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Affiliation(s)
- Phoebe Asplin
- EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Rebecca Mancy
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Thomas Finnie
- Data, Analytics and Surveillance, UK Health Security Agency, London, UK
| | - Fergus Cumming
- Foreign, Commonwealth and Development Office, London, UK
| | - Matt J. Keeling
- Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- School of Life Sciences, University of Glasgow, Glasgow, UK
| | - Edward M. Hill
- Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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4
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Alòs J, Ansótegui C, Dotu I, García-Herranz M, Pastells P, Torres E. ePyDGGA: automatic configuration for fitting epidemic curves. Sci Rep 2024; 14:784. [PMID: 38191771 PMCID: PMC10774272 DOI: 10.1038/s41598-023-43958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/30/2023] [Indexed: 01/10/2024] Open
Abstract
Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .
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Affiliation(s)
- Josep Alòs
- Logic and Optimization Group, University of Lleida, Lleida, Spain.
| | - Carlos Ansótegui
- Logic and Optimization Group, University of Lleida, Lleida, Spain.
| | | | | | | | - Eduard Torres
- Logic and Optimization Group, University of Lleida, Lleida, Spain
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5
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Case BKM, Young JG, Hébert-Dufresne L. Accurately summarizing an outbreak using epidemiological models takes time. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230634. [PMID: 37771961 PMCID: PMC10523082 DOI: 10.1098/rsos.230634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.
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Affiliation(s)
- B. K. M. Case
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
| | - Jean-Gabriel Young
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT 05405, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
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6
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Ghosh S, Birrell PJ, De Angelis D. An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling. PLoS Comput Biol 2023; 19:e1011088. [PMID: 37200386 PMCID: PMC10231796 DOI: 10.1371/journal.pcbi.1011088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 05/31/2023] [Accepted: 04/10/2023] [Indexed: 05/20/2023] Open
Abstract
Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant approach to capturing environmental stochasticity is to model the force of infection as a stochastic process. However, inference in this context requires solving a computationally expensive "missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the "missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task. We illustrate the merit of this approach through three examples: modelling influenza using a canonical SIR model, capturing seasonality using a SIRS model, and the modelling of COVID-19 pandemic using a multi-type SEIR model.
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Affiliation(s)
- Sanmitra Ghosh
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Paul J. Birrell
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- UK Health Security Agency, London, United Kingdom
| | - Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- UK Health Security Agency, London, United Kingdom
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7
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Engebretsen S, Diz-Lois Palomares A, Rø G, Kristoffersen AB, Lindstrøm JC, Engø-Monsen K, Kamineni M, Hin Chan LY, Dale Ø, Midtbø JE, Stenerud KL, Di Ruscio F, White R, Frigessi A, de Blasio BF. A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. PLoS Comput Biol 2023; 19:e1010860. [PMID: 36689468 PMCID: PMC9894546 DOI: 10.1371/journal.pcbi.1010860] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/02/2023] [Accepted: 01/08/2023] [Indexed: 01/24/2023] Open
Abstract
The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
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Affiliation(s)
| | | | - Gunnar Rø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | | | | | - Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Louis Yat Hin Chan
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Telenor Norge AS Fornebu, Norway
| | | | - Francesco Di Ruscio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Richard White
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
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8
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Mechanistic models of Rift Valley fever virus transmission: A systematic review. PLoS Negl Trop Dis 2022; 16:e0010339. [PMID: 36399500 PMCID: PMC9718419 DOI: 10.1371/journal.pntd.0010339] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 12/02/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022] Open
Abstract
Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.
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9
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Pooley CM, Doeschl-Wilson AB, Marion G. Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210298. [PMID: 35965466 PMCID: PMC9376725 DOI: 10.1098/rsta.2021.0298] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 03/10/2022] [Indexed: 05/08/2023]
Abstract
Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Christopher M. Pooley
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
| | | | - Glenn Marion
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
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10
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Parag KV, Donnelly CA, Zarebski AE. Quantifying the information in noisy epidemic curves. NATURE COMPUTATIONAL SCIENCE 2022; 2:584-594. [PMID: 38177483 DOI: 10.1038/s43588-022-00313-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/08/2022] [Indexed: 01/06/2024]
Abstract
Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.
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Affiliation(s)
- Kris V Parag
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK.
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
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11
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Keeling MJ, Dyson L, Guyver-Fletcher G, Holmes A, Semple MG, Tildesley MJ, Hill EM. Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. Stat Methods Med Res 2022; 31:1716-1737. [PMID: 35037796 PMCID: PMC9465059 DOI: 10.1177/09622802211070257] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, [Formula: see text], has taken on special significance in terms of the general understanding of whether the epidemic is under control ([Formula: see text]). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.
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Affiliation(s)
- Matt J Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Glen Guyver-Fletcher
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Midlands Integrative Biosciences Training Partnership, School of Life Sciences, 2707University of Warwick, UK
| | - Alex Holmes
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Mathematics for Real World Systems Centre for Doctoral Training, Mathematics Institute, 2707University of Warwick, UK
| | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, 4591University of Liverpool, UK
- Respiratory Medicine, Alder Hey Children's Hospital, Institute in The Park, 4591University of Liverpool, Alder Hey Children's Hospital, Liverpool, UK
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Edward M Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
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12
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Nicholson G, Blangiardo M, Briers M, Diggle PJ, Fjelde TE, Ge H, Goudie RJB, Jersakova R, King RE, Lehmann BCL, Mallon AM, Padellini T, Teh YW, Holmes C, Richardson S. Interoperability of statistical models in pandemic preparedness: principles and reality. Stat Sci 2022; 37:183-206. [PMID: 35664221 PMCID: PMC7612804 DOI: 10.1214/22-sts854] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.
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Affiliation(s)
| | - Marta Blangiardo
- MRC Centre for Environment and Health, Dept of Epidemiology and Biostatistics, Imperial College London
| | | | - Peter J Diggle
- CHICAS, Lancaster Medical School, Lancaster University, UK
| | | | - Hong Ge
- Department of Engineering, University of Cambridge, UK
| | | | | | | | | | | | - Tullia Padellini
- MRC Centre for Environment and Health, Dept of Epidemiology and Biostatistics, Imperial College London
| | | | - Chris Holmes
- University of Oxford, UK
- The Alan Turing Institute, London, UK
- MRC Harwell Institute, Harwell, UK
| | - Sylvia Richardson
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, University of Cambridge, UK
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13
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Campbell H, de Valpine P, Maxwell L, de Jong VMT, Debray TPA, Jaenisch T, Gustafson P. Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Perry de Valpine
- Department of Environmental Science, Policy, and Management, University of California
| | - Lauren Maxwell
- Heidelberg Institute for Global Health, Heidelberg University Hospital
| | - Valentijn M. T. de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University
| | - Thomas P. A. Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University
| | - Thomas Jaenisch
- Heidelberg Institute for Global Health, Heidelberg University Hospital
| | - Paul Gustafson
- Department of Statistics, University of British Columbia
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14
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Swallow B, Birrell P, Blake J, Burgman M, Challenor P, Coffeng LE, Dawid P, De Angelis D, Goldstein M, Hemming V, Marion G, McKinley TJ, Overton CE, Panovska-Griffiths J, Pellis L, Probert W, Shea K, Villela D, Vernon I. Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics 2022; 38:100547. [PMID: 35180542 PMCID: PMC7612598 DOI: 10.1016/j.epidem.2022.100547] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/22/2021] [Accepted: 02/09/2022] [Indexed: 12/15/2022] Open
Abstract
The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement. We also postulate priorities in estimation methodology to facilitate preparation for future pandemics.
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Affiliation(s)
- Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK.
| | - Paul Birrell
- Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Joshua Blake
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Mark Burgman
- Centre for Environmental Policy, Imperial College London, London, UK
| | - Peter Challenor
- The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Luc E Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Philip Dawid
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Michael Goldstein
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
| | - Victoria Hemming
- Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada
| | - Glenn Marion
- Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK
| | - Trevelyan J McKinley
- College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Christopher E Overton
- Department of Mathematics, University of Manchester, Manchester, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
| | - Will Probert
- The Big Data Institute, University of Oxford, Oxford, UK
| | - Katriona Shea
- Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA
| | - Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Ian Vernon
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
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15
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Zarebski AE, du Plessis L, Parag KV, Pybus OG. A computationally tractable birth-death model that combines phylogenetic and epidemiological data. PLoS Comput Biol 2022; 18:e1009805. [PMID: 35148311 PMCID: PMC8903285 DOI: 10.1371/journal.pcbi.1009805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 03/08/2022] [Accepted: 01/05/2022] [Indexed: 11/19/2022] Open
Abstract
Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.
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Affiliation(s)
| | - Louis du Plessis
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Kris Varun Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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16
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Abstract
The world is currently overwhelmed with the perils of the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. As of May 18, 2020, there were 4,819,102 confirmed cases, of which there were 316,959 deaths worldwide. The devastating effects of the COVID-19 pandemic on the world economy are more grievous than many natural disasters like earthquakes and tsunamis in history. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been essential to assist policymakers and health practitioners in the public and private health sector in providing an efficient way of alleviating the effects of the pandemic across continents. Scholars have steadily worked to provide timely information. Nevertheless, there is a lack of information on which insights can be derived from all these endeavors, especially with regard to modeling and prediction techniques. In this study, we used a literature synthesis approach to provide a narrative review of the current research efforts geared toward predicting the spread of COVID-19 across continents. Such information is useful to provide a global perspective of the virus particularly with regard to modeling and prediction techniques and their outcomes. A total of 69 peer-reviewed articles were reviewed. We found that most articles were from Asia (34.8%) and Europe (23.2%), followed by North America (14.5%), and very few emanated from other continents including Africa and Australia (6.8% each), while no study was reported in Antarctica. Most of the modeling and predictions were based on compartmental epidemiologic models and a few used advanced machine learning techniques. While some models have accurately predicted the end of the epidemic in some countries, other predictions strongly deviate from reality. Interestingly, some studies showed that combining artificial intelligence with classical compartmental models provides a better prediction of the disease spread. Assumptions made when parameterizing the models might be wrong and might not suit the local contexts and might partly explain the observed deviation from the reality on the ground. Furthermore, lack of publicly available key data such as age, gender, comorbidity, and historical medical data of cases and deaths in some continents could limit researchers in addressing some essential aspects of the virus spread and its consequences.
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17
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Keeling MJ, Dyson L, Guyver-Fletcher G, Holmes A, Semple MG, ISARIC4C Investigators, Tildesley MJ, Hill EM. Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.08.04.20163782. [PMID: 32817970 PMCID: PMC7430615 DOI: 10.1101/2020.08.04.20163782] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R < 1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first-wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.
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Affiliation(s)
- Matt J. Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Glen Guyver-Fletcher
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Midlands Integrative Biosciences Training Partnership, School of Life Sciences, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Alex Holmes
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Mathematics for Real World Systems Centre for Doctoral Training, Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
- Respiratory Medicine, Alder Hey Children’s Hospital, Institute in The Park, University of Liverpool, Alder Hey Children’s Hospital, Liverpool L12 2AP, United Kingdom
| | | | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Edward M. Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
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18
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Ferrari L, Gerardi G, Manzi G, Micheletti A, Nicolussi F, Biganzoli E, Salini S. Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6563. [PMID: 34207174 PMCID: PMC8296340 DOI: 10.3390/ijerph18126563] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 12/27/2022]
Abstract
In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.
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Affiliation(s)
- Luisa Ferrari
- Department of Statistical Science, University College London, London WC1E 6BT, UK;
| | - Giuseppe Gerardi
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy;
| | - Giancarlo Manzi
- Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy; (F.N.); (S.S.)
| | - Alessandra Micheletti
- Department of Environmental Science and Policy and Data Science Research Center, University of Milan, 20122 Milan, Italy;
| | - Federica Nicolussi
- Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy; (F.N.); (S.S.)
| | - Elia Biganzoli
- Department of Clinical Sciences and Community Health and Data Science Research Center, University of Milan, 20122 Milan, Italy;
| | - Silvia Salini
- Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy; (F.N.); (S.S.)
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19
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Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities. Bull Math Biol 2021; 83:57. [PMID: 33835296 PMCID: PMC8033284 DOI: 10.1007/s11538-021-00895-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023]
Abstract
As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.
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20
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Anđelić N, Baressi Šegota S, Lorencin I, Mrzljak V, Car Z. Estimation of COVID-19 epidemic curves using genetic programming algorithm. Health Informatics J 2021; 27:1460458220976728. [PMID: 33459107 DOI: 10.1177/1460458220976728] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.
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Affiliation(s)
- Nikola Anđelić
- University of Rijeka, Faculty of Engineering, Rijeka, Croatia
| | | | - Ivan Lorencin
- University of Rijeka, Faculty of Engineering, Rijeka, Croatia
| | - Vedran Mrzljak
- University of Rijeka, Faculty of Engineering, Rijeka, Croatia
| | - Zlatan Car
- University of Rijeka, Faculty of Engineering, Rijeka, Croatia
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21
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Luz PM, Struchiner CJ, Kim SY, Minamisava R, Andrade ALS, Sanderson C, Russell LB, Toscano CM. Modeling the cost-effectiveness of maternal acellular pertussis immunization (aP) in different socioeconomic settings: A dynamic transmission model of pertussis in three Brazilian states. Vaccine 2021; 39:125-136. [PMID: 33303180 PMCID: PMC7738757 DOI: 10.1016/j.vaccine.2020.09.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 07/01/2020] [Accepted: 09/01/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVES Using dynamic transmission models we evaluated the health and cost outcomes of adding acellular pertussis (aP) vaccination of pregnant women to infant vaccination in three Brazilian states that represent different socioeconomic conditions. The primary objective was to determine whether the same model structure could be used to represent pertussis disease dynamics in differing socioeconomic conditions. METHODS We tested three model structures (SIR, SIRS, SIRSIs) to represent population-level transmission in three socio-demographically distinct Brazilian states: São Paulo, Paraná and Bahia. Two strategies were evaluated: infant wP vaccination alone versus maternal aP immunization plus infant wP vaccination. Model projections for 2014-2029 include outpatient and inpatient pertussis cases, pertussis deaths, years of life lost, disability-adjusted life-years (DALYs) lost, and costs (in 2014 USD) of maternal aP vaccination, infant vaccination, and pertussis medical treatment. Incremental cost per DALY averted is presented from the perspective of the Brazilian National Health System. RESULTS Based on goodness-of-fit statistics, the SIRSIs model fit best, although it had only a modest improvement in statistical quantitative assessments relative to the SIRS model. For all three Brazilian states, maternal aP immunization led to higher costs but also saved infant lives and averted DALYs. The 2014 USD cost/DALY averted was $3068 in Sao Paulo, $2962 in Parana, and $2022 in Bahia. These results were robust in sensitivity analyses with the incremental cost-effectiveness ratios exceeding per capita gross regional product only when the probability that a pertussis case is reported was assumed higher than base case implying more overt cases and deaths and therefore more medical costs. CONCLUSIONS The same model structure fit all three states best, supporting the idea that the disease behaves similarly across different socioeconomic conditions. We also found that immunization of pregnant women with aP is cost-effective in diverse Brazilian states.
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Affiliation(s)
- Paula M Luz
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
| | - Claudio J Struchiner
- Escola de Matemática Aplicada, Fundação Getúlio Vargas, Praia de Botafogo, 190, Rio de Janeiro, Brazil
| | - Sun-Young Kim
- Seoul National University, Department of Healthcare Management and Policy, SNU Graduate School of Public Health, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Ruth Minamisava
- Faculdade de Enfermagem, Universidade Federal de Goiás, Goiania, Goias, Brazil
| | - Ana Lucia S Andrade
- Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiania, Goiás, Brazil
| | - Colin Sanderson
- London School of Hygiene and Tropical Medicine, Department of Health Services Research and Policy, 15-17 Tavistock Place, London WC1H 9SH, United Kingdom
| | - Louise B Russell
- University of Pennsylvania, Department of Medical Ethics and Health Policy, 423 Guardian Drive, Philadelphia PA 19104, USA
| | - Cristiana M Toscano
- Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiania, Goiás, Brazil
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22
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Snyder RE, Feiszli T, Foss L, Messenger S, Fang Y, Barker CM, Reisen WK, Vugia DJ, Padgett KA, Kramer VL. West Nile virus in California, 2003-2018: A persistent threat. PLoS Negl Trop Dis 2020; 14:e0008841. [PMID: 33206634 PMCID: PMC7710070 DOI: 10.1371/journal.pntd.0008841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 12/02/2020] [Accepted: 09/24/2020] [Indexed: 02/06/2023] Open
Abstract
The California Arbovirus Surveillance Program was initiated over 50 years ago to track endemic encephalitides and was enhanced in 2000 to include West Nile virus (WNV) infections in humans, mosquitoes, sentinel chickens, dead birds and horses. This comprehensive statewide program is a function of strong partnerships among the California Department of Public Health (CDPH), the University of California, and local vector control and public health agencies. This manuscript summarizes WNV surveillance data in California since WNV was first detected in 2003 in southern California. From 2003 through 2018, 6,909 human cases of WNV disease, inclusive of 326 deaths, were reported to CDPH, as well as 730 asymptomatic WNV infections identified during screening of blood and organ donors. Of these, 4,073 (59.0%) were reported as West Nile neuroinvasive disease. California's WNV disease burden comprised 15% of all cases that were reported to the U.S. Centers for Disease Control and Prevention during this time, more than any other state. Additionally, 1,299 equine WNV cases were identified, along with detections of WNV in 23,322 dead birds, 31,695 mosquito pools, and 7,340 sentinel chickens. Annual enzootic detection of WNV typically preceded detection in humans and prompted enhanced intervention to reduce the risk of WNV transmission. Peak WNV activity occurred from July through October in the Central Valley and southern California. Less than five percent of WNV activity occurred in other regions of the state or outside of this time. WNV continues to be a major threat to public and wild avian health in California, particularly in southern California and the Central Valley during summer and early fall months. Local and state public health partners must continue statewide human and mosquito surveillance and facilitate effective mosquito control and bite prevention measures.
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Affiliation(s)
- Robert E. Snyder
- California Department of Public Health, Vector-Borne Disease Section, Richmond and Sacramento, California, United States of America
| | - Tina Feiszli
- California Department of Public Health, Vector-Borne Disease Section, Richmond and Sacramento, California, United States of America
| | - Leslie Foss
- California Department of Public Health, Vector-Borne Disease Section, Richmond and Sacramento, California, United States of America
| | - Sharon Messenger
- California Department of Public Health, Division of Communicable Disease Control, Richmond, California, United States of America
| | - Ying Fang
- Department of Pathology, Microbiology & Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - Christopher M. Barker
- Department of Pathology, Microbiology & Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - William K. Reisen
- Department of Pathology, Microbiology & Immunology, School of Veterinary Medicine, University of California, Davis, California, United States of America
| | - Duc J. Vugia
- California Department of Public Health, Division of Communicable Disease Control, Richmond, California, United States of America
| | - Kerry A. Padgett
- California Department of Public Health, Vector-Borne Disease Section, Richmond and Sacramento, California, United States of America
| | - Vicki L. Kramer
- California Department of Public Health, Vector-Borne Disease Section, Richmond and Sacramento, California, United States of America
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23
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Chen S, Owolabi Y, Li A, Lo E, Robinson P, Janies D, Lee C, Dulin M. Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems. PLoS One 2020; 15:e0238186. [PMID: 33057348 PMCID: PMC7561140 DOI: 10.1371/journal.pone.0238186] [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: 01/23/2020] [Accepted: 08/11/2020] [Indexed: 11/25/2022] Open
Abstract
Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.
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Affiliation(s)
- Shi Chen
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America
- School of Data Science, University of North Carolina Charlotte, Charlotte, NC, United States of America
| | - Yakubu Owolabi
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America
- Division of HIV and TB, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Ang Li
- State Key Laboratory of Vegetation and Environmental Change, Chinese Academy of Sciences, Beijing, China
| | - Eugenia Lo
- Department of Biological Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America
| | - Patrick Robinson
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America
- Academy of Population Health Innovation, University of North Carolina Charlotte, Charlotte, NC, United States of America
| | - Daniel Janies
- Department of Bioinformatics, University of North Carolina Charlotte, Charlotte, NC, United States of America
| | - Chihoon Lee
- School of Business, Stevens Institute of Technology, Hoboken, NJ, United States of America
| | - Michael Dulin
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America
- Academy of Population Health Innovation, University of North Carolina Charlotte, Charlotte, NC, United States of America
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24
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van Boven M, Teirlinck AC, Meijer A, Hooiveld M, van Dorp CH, Reeves RM, Campbell H, van der Hoek W. Estimating Transmission Parameters for Respiratory Syncytial Virus and Predicting the Impact of Maternal and Pediatric Vaccination. J Infect Dis 2020; 222:S688-S694. [PMID: 32821916 PMCID: PMC7751153 DOI: 10.1093/infdis/jiaa424] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Respiratory syncytial virus (RSV) is a leading cause of respiratory tract illness in young children and a major cause of hospital admissions globally. METHODS Here we fit age-structured transmission models with immunity propagation to data from the Netherlands (2012-2017). Data included nationwide hospitalizations with confirmed RSV, general practitioner (GP) data on attendance for care from acute respiratory infection, and virological testing of acute respiratory infections at the GP. The transmission models, equipped with key parameter estimates, were used to predict the impact of maternal and pediatric vaccination. RESULTS Estimates of the basic reproduction number were generally high (R0 > 10 in scenarios with high statistical support), while susceptibility was estimated to be low in nonelderly adults (<10% in persons 20-64 years) and was higher in older adults (≥65 years). Scenario analyses predicted that maternal vaccination reduces the incidence of infection in vulnerable infants (<1 year) and shifts the age of first infection from infants to young children. CONCLUSIONS Pediatric vaccination is expected to reduce the incidence of infection in infants and young children (0-5 years), slightly increase incidence in 5 to 9-year-old children, and have minor indirect benefits.
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Affiliation(s)
- Michiel van Boven
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Anne C Teirlinck
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Adam Meijer
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
| | | | - Christiaan H van Dorp
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Rachel M Reeves
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Wim van der Hoek
- Centre for Infectious Disease Control, National institute for Public Health and the Environment, Bilthoven, the Netherlands
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25
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Alahmadi A, Belet S, Black A, Cromer D, Flegg JA, House T, Jayasundara P, Keith JM, McCaw JM, Moss R, Ross JV, Shearer FM, Tun STT, Walker CR, White L, Whyte JM, Yan AWC, Zarebski AE. Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges. Epidemics 2020; 32:100393. [PMID: 32674025 DOI: 10.1016/j.epidem.2020.100393] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/25/2020] [Indexed: 12/16/2022] Open
Abstract
Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.
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Affiliation(s)
- Amani Alahmadi
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia
| | - Sarah Belet
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Andrew Black
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Deborah Cromer
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, Australia and School of Mathematics and Statistics, UNSW Sydney, Sydney, Australia
| | - Jennifer A Flegg
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK; IBM Research, Hartree Centre, Sci-Tech Daresbury, Warrington, UK.
| | | | - Jonathan M Keith
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - James M McCaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.
| | - Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Joshua V Ross
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).
| | - Freya M Shearer
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sai Thein Than Tun
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, UK
| | - Camelia R Walker
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia
| | - Lisa White
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, UK
| | - Jason M Whyte
- Centre of Excellence for Biosecurity Risk Analysis (CEBRA), School of BioSciences, University of Melbourne, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Ada W C Yan
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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26
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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: 14.8] [Reference Citation Analysis] [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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27
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Ezanno P, Andraud M, Beaunée G, Hoch T, Krebs S, Rault A, Touzeau S, Vergu E, Widgren S. How mechanistic modelling supports decision making for the control of enzootic infectious diseases. Epidemics 2020; 32:100398. [PMID: 32622313 DOI: 10.1016/j.epidem.2020.100398] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/07/2020] [Accepted: 05/29/2020] [Indexed: 12/28/2022] Open
Abstract
Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.
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Affiliation(s)
- P Ezanno
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - M Andraud
- Unité épidémiologie et bien-être du porc, Anses Laboratoire de Ploufragan-Plouzané, Ploufragan, France.
| | - G Beaunée
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - T Hoch
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Krebs
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - A Rault
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Touzeau
- INRAE, CNRS, Université Côte d'Azur, ISA, France; Inria, INRAE, CNRS, Université Paris Sorbonne, Université Côte d'Azur, BIOCORE, France.
| | - E Vergu
- INRAE, Université Paris-Saclay, MaIAGE, 78350 Jouy-en-Josas, France.
| | - S Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden.
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28
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Ashofteh A, Bravo JM. A study on the quality of novel coronavirus (COVID-19) official datasets. ACTA ACUST UNITED AC 2020. [DOI: 10.3233/sji-200674] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Jorge M. Bravo
- NOVA Information Management School, Portugal
- Université Paris-Dauphine PSL, Paris, France
- MagIC
- CEFAGE-UE
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29
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Ryu S, Chun BC. An interim review of the epidemiological characteristics of 2019 novel coronavirus. Epidemiol Health 2020; 42:e2020006. [PMID: 32023775 PMCID: PMC7011107 DOI: 10.4178/epih.e2020006] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 02/06/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES The 2019 novel coronavirus (2019-nCoV) from Wuhan, China is currently recognized as a public health emergency of global concern. METHODS We reviewed the currently available literature to provide up-to-date guidance on control measures to be implemented by public health authorities. RESULTS Some of the epidemiological characteristics of 2019-nCoV have been identified. However, there remain considerable uncertainties, which should be considered when providing guidance to public health authorities on control measures. CONCLUSIONS Additional studies incorporating more detailed information from confirmed cases would be valuable.
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Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, Korea
| | - Byung Chul Chun
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
| | -
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, Korea
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30
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Singh M, Sarkhel P, Kang GJ, Marathe A, Boyle K, Murray-Tuite P, Abbas KM, Swarup S. Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. BMC Infect Dis 2019; 19:221. [PMID: 30832594 PMCID: PMC6399923 DOI: 10.1186/s12879-019-3703-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 01/09/2019] [Indexed: 01/29/2023] Open
Abstract
Background Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. Methods We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia. Results We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions. Conclusions By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza. Electronic supplementary material The online version of this article (10.1186/s12879-019-3703-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meghendra Singh
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, 24060, Virginia, USA
| | - Prasenjit Sarkhel
- Department of Economics, University of Kalyani, Nadia, 741235, West Bengal, India
| | - Gloria J Kang
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, 24060, Virginia, USA.,Department of Population Health Sciences, Virginia Tech, Blacksburg, 24060, Virginia, USA
| | - Achla Marathe
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22908, Virginia, USA. .,Department of Public Health Sciences, University of Virginia, Charlottesville, 22908, Virginia, USA.
| | - Kevin Boyle
- Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, 24060, Virginia, USA
| | - Pamela Murray-Tuite
- Department of Civil Engineering, Clemson University, Clemson, 29634, South Carolina, USA
| | - Kaja M Abbas
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E7HT, UK
| | - Samarth Swarup
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22908, Virginia, USA
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31
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Moss R, Zarebski AE, Carlson SJ, McCaw JM. Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts. Trop Med Infect Dis 2019; 4:E12. [PMID: 30641917 PMCID: PMC6473244 DOI: 10.3390/tropicalmed4010012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/08/2019] [Accepted: 01/08/2019] [Indexed: 11/29/2022] Open
Abstract
For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.
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Affiliation(s)
- Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville 3052, Australia.
| | | | | | - James M McCaw
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville 3052, Australia.
- School of Mathematics and Statistics, The University of Melbourne, Parkville 3052, Australia.
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville 3052, Australia.
- Victorian Infectious Diseases Reference Laboratory Epidemiology Unit, Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Melbourne 3000, Australia.
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32
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An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients. Epidemiology 2018; 28:484-491. [PMID: 28252453 DOI: 10.1097/ede.0000000000000646] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data. METHODS We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups. RESULTS The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods. CONCLUSIONS The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.
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Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control. Epidemiology 2018; 28:136-144. [PMID: 27748685 DOI: 10.1097/ede.0000000000000571] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, although they are often modified by other, nontemporal factors. For infectious diseases, in which overall incidence often varies substantially over time, the patterns of temporal changes in relative risks can inform our understanding of basic epidemiologic questions. For example, recent studies suggest that temporal changes in relative risks of infection over the course of an epidemic cycle can both be used to identify population groups that drive infectious disease outbreaks, and help elucidate differences in the effect of vaccination against infection (that is relevant to transmission control) compared with its effect against disease episodes (that reflects individual protection). Patterns of change in the age groups affected over the course of seasonal outbreaks can provide clues to the types of pathogens that could be responsible for diseases for which an infectious cause is suspected. Changing apparent efficacy of vaccines during trials may provide clues to the vaccine's mode of action and/or indicate risk heterogeneity in the trial population. Declining importance of unusual behavioral risk factors may be a signal of increased local transmission of an infection. We review these developments and the related public health implications.
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Abstract
In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.
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Affiliation(s)
- Paul J. Birrell
- Paul Birrell is a Senior Investigator Statistician at the MRC Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom
| | - Daniela De Angelis
- Daniela De Angelis is a Programme Leader at the MRC Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom
| | - Anne M. Presanis
- Anne Presanis is a Senior Investigator Statistician at the MRC Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom
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House T, Ford A, Lan S, Bilson S, Buckingham-Jeffery E, Girolami M. Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry. J R Soc Interface 2017; 13:rsif.2016.0279. [PMID: 27558850 PMCID: PMC5014059 DOI: 10.1098/rsif.2016.0279] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/25/2016] [Indexed: 12/14/2022] Open
Abstract
Infectious diseases exert a large and in many contexts growing burden on human health, but violate most of the assumptions of classical epidemiological statistics and hence require a mathematically sophisticated approach. Viral shedding data are collected during human studies—either where volunteers are infected with a disease or where existing cases are recruited—in which the levels of live virus produced over time are measured. These have traditionally been difficult to analyse due to strong, complex correlations between parameters. Here, we show how a Bayesian approach to the inverse problem together with modern Markov chain Monte Carlo algorithms based on information geometry can overcome these difficulties and yield insights into the disease dynamics of two of the most prevalent human pathogens—influenza and norovirus—as well as Ebola virus disease.
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Affiliation(s)
- Thomas House
- School of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Ashley Ford
- School of Mathematics, University of Bristol, Bristol BS8 1TW, UK
| | - Shiwei Lan
- Department of Statistics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Samuel Bilson
- Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Elizabeth Buckingham-Jeffery
- Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK Complexity Science Doctoral Training Centre, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Mark Girolami
- Department of Statistics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
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Heslop DJ, Chughtai AA, Bui CM, MacIntyre CR. Publicly available software tools for decision-makers during an emergent epidemic-Systematic evaluation of utility and usability. Epidemics 2017; 21:1-12. [PMID: 28576351 DOI: 10.1016/j.epidem.2017.04.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 01/24/2017] [Accepted: 04/19/2017] [Indexed: 12/11/2022] Open
Abstract
Epidemics and emerging infectious diseases are becoming an increasing threat to global populations-challenging public health practitioners, decision makers and researchers to plan, prepare, identify and respond to outbreaks in near real-timeframes. The aim of this research is to evaluate the range of public domain and freely available software epidemic modelling tools. Twenty freely utilisable software tools underwent assessment of software usability, utility and key functionalities. Stochastic and agent based tools were found to be highly flexible, adaptable, had high utility and many features, but low usability. Deterministic tools were highly usable with average to good levels of utility.
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Affiliation(s)
- David James Heslop
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
| | - Abrar Ahmad Chughtai
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | - Chau Minh Bui
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | - C Raina MacIntyre
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia; College of Community Solutions and Public Affairs, Arizona State University, USA
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Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study. PLoS Comput Biol 2017; 13:e1005416. [PMID: 28263987 PMCID: PMC5358897 DOI: 10.1371/journal.pcbi.1005416] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 03/20/2017] [Accepted: 02/16/2017] [Indexed: 02/06/2023] Open
Abstract
Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies. Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.
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Price MJ, Ades AE, Soldan K, Welton NJ, Macleod J, Simms I, DeAngelis D, Turner KM, Horner PJ. The natural history of Chlamydia trachomatis infection in women: a multi-parameter evidence synthesis. Health Technol Assess 2016; 20:1-250. [PMID: 27007215 DOI: 10.3310/hta20220] [Citation(s) in RCA: 298] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The evidence base supporting the National Chlamydia Screening Programme, initiated in 2003, has been questioned repeatedly, with little consensus on modelling assumptions, parameter values or evidence sources to be used in cost-effectiveness analyses. The purpose of this project was to assemble all available evidence on the prevalence and incidence of Chlamydia trachomatis (CT) in the UK and its sequelae, pelvic inflammatory disease (PID), ectopic pregnancy (EP) and tubal factor infertility (TFI) to review the evidence base in its entirety, assess its consistency and, if possible, arrive at a coherent set of estimates consistent with all the evidence. METHODS Evidence was identified using 'high-yield' strategies. Bayesian Multi-Parameter Evidence Synthesis models were constructed for separate subparts of the clinical and population epidemiology of CT. Where possible, different types of data sources were statistically combined to derive coherent estimates. Where evidence was inconsistent, evidence sources were re-interpreted and new estimates derived on a post-hoc basis. RESULTS An internally coherent set of estimates was generated, consistent with a multifaceted evidence base, fertility surveys and routine UK statistics on PID and EP. Among the key findings were that the risk of PID (symptomatic or asymptomatic) following an untreated CT infection is 17.1% [95% credible interval (CrI) 6% to 29%] and the risk of salpingitis is 7.3% (95% CrI 2.2% to 14.0%). In women aged 16-24 years, screened at annual intervals, at best, 61% (95% CrI 55% to 67%) of CT-related PID and 22% (95% CrI 7% to 43%) of all PID could be directly prevented. For women aged 16-44 years, the proportions of PID, EP and TFI that are attributable to CT are estimated to be 20% (95% CrI 6% to 38%), 4.9% (95% CrI 1.2% to 12%) and 29% (95% CrI 9% to 56%), respectively. The prevalence of TFI in the UK in women at the end of their reproductive lives is 1.1%: this is consistent with all PID carrying a relatively high risk of reproductive damage, whether diagnosed or not. Every 1000 CT infections in women aged 16-44 years, on average, gives rise to approximately 171 episodes of PID and 73 of salpingitis, 2.0 EPs and 5.1 women with TFI at age 44 years. CONCLUSIONS AND RESEARCH RECOMMENDATIONS The study establishes a set of interpretations of the major studies and study designs, under which a coherent set of estimates can be generated. CT is a significant cause of PID and TFI. CT screening is of benefit to the individual, but detection and treatment of incident infection may be more beneficial. Women with lower abdominal pain need better advice on when to seek early medical attention to avoid risk of reproductive damage. The study provides new insights into the reproductive risks of PID and the role of CT. Further research is required on the proportions of PID, EP and TFI attributable to CT to confirm predictions made in this report, and to improve the precision of key estimates. The cost-effectiveness of screening should be re-evaluated using the findings of this report. FUNDING The Medical Research Council grant G0801947.
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Affiliation(s)
- Malcolm J Price
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - A E Ades
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Kate Soldan
- Public Health England (formerly Health Protection Agency), Colindale, London, UK
| | - Nicky J Welton
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - John Macleod
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Ian Simms
- Public Health England (formerly Health Protection Agency), Colindale, London, UK
| | - Daniela DeAngelis
- Public Health England (formerly Health Protection Agency), Colindale, London, UK.,Medical Research Council Biostatistics Unit, Cambridge, UK
| | | | - Paddy J Horner
- School of Social and Community Medicine, University of Bristol, Bristol, UK.,Bristol Sexual Health Centre, University Hospital Bristol NHS Foundation Trust, Bristol, UK
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Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systems. Epidemiol Infect 2016; 145:156-169. [DOI: 10.1017/s0950268816002053] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
SUMMARYAccurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4–6 weeks in advance. We also show that makingsimultaneoususe of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a ‘ground truth’ for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.
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Coudeville L, Baurin N, L'Azou M, Guy B. Potential impact of dengue vaccination: Insights from two large-scale phase III trials with a tetravalent dengue vaccine. Vaccine 2016; 34:6426-6435. [PMID: 27601343 DOI: 10.1016/j.vaccine.2016.08.050] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 08/11/2016] [Accepted: 08/16/2016] [Indexed: 11/25/2022]
Abstract
BACKGROUND A tetravalent dengue vaccine demonstrated its protective efficacy in two phase III efficacy studies. Results from these studies were used to derive vaccination impact in the five Asian (Indonesia, Malaysia, Philippines, Thailand, Vietnam) and the five Latin American countries (Brazil, Colombia, Honduras, Mexico and Puerto Rico) participating in these trials. METHODS Vaccination impact was investigated with an age-structured, host-vector, serotype-specific compartmental model. Parameters related to vaccine efficacy and levels of dengue transmission were estimated using data collected during the phase III efficacy studies. Several vaccination programs, including routine vaccination at different ages with and without large catch-up campaigns, were investigated. RESULTS All vaccination programs explored translated into significant reductions in dengue cases at the population level over the first 10years following vaccine introduction and beyond. The most efficient age for vaccination varied according to transmission intensity and 9years was close to the most efficient age across all settings. The combination of routine vaccination and large catch-up campaigns was found to enable a rapid reduction of dengue burden after vaccine introduction. CONCLUSION Our analysis suggests that dengue vaccination can significantly reduce the public health impact of dengue in countries where the disease is endemic.
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Affiliation(s)
| | - Nicolas Baurin
- Vaccination Value Modeling, Sanofi Pasteur, Lyon, France
| | - Maïna L'Azou
- Global Epidemiology, Sanofi Pasteur, Lyon, France
| | - Bruno Guy
- Research & Development, Sanofi Pasteur, Lyon, France
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42
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de Silva AM, Gkolia P, Carpenter L, Cole D. Developing a model to assess community-level risk of oral diseases for planning public dental services in Australia. BMC Oral Health 2016; 16:45. [PMID: 27036224 PMCID: PMC4815130 DOI: 10.1186/s12903-016-0200-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/16/2016] [Indexed: 11/16/2022] Open
Abstract
Background Poor oral health is a chronic condition that can be extremely costly to manage. In Australia, publicly funded dental services are provided to community members deemed to be eligible—those who are socio-economically disadvantaged or determined to be at higher risk of dental disease. Historically public dental services have nominally been allocated based on the size of the eligible population in a geographic area. This approach has been largely inadequate for reducing disparities in dental disease, primarily because the approach is treatment-focused, and oral health is influenced by a variety of interacting factors. This paper describes the developmental process of a multi-dimensional community-level risk assessment model, to profile a community’s risk of poor oral health. Methods A search of the evidence base was conducted to identify robust frameworks for conceptualisation of risk factors and associated performance indicators. Government and other agency websites were also searched to identify publicly available data assets with items relevant to oral diseases. Data quality and analysis considerations were assessed for the use of mixed data sources. Results Several frameworks and associated indicator sets (twelve national and eight state-wide data collections with relevant indicators) were identified. Determination of the system inputs for the Model were primarily informed by the World Health Organisation’s (WHO) operational model for an Integrated Oral Health-Chronic Disease Prevention System, and Australia’s National Oral Health Plan 2004–2013. Data quality and access informed the final selection of indicators. Conclusions Despite limitations in the quality and regularity of data collections, there are numerous data sources available that provide the required data inputs for community-level risk assessment for oral health. Assessing risk in this way will enhance our ability to deliver appropriate public oral health care services and address the uneven distribution of oral disease across the social gradient.
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Affiliation(s)
- Andrea M de Silva
- Centre for Applied Oral Health Research, Dental Health Services Victoria, 720 Swanston Street, Carlton, 3053, Australia. .,Melbourne Dental School, University of Melbourne, Carlton, 3053, Australia.
| | - Panagiota Gkolia
- Infectious Diseases Division, Department of Internal Medicine I, University Hospital Tübingen, Otfried-Müller-Street, Tübingen, 72076, Germany
| | - Lauren Carpenter
- Jack Brockhoff Child Health and Wellbeing Program, Centre for Health Equity, The Melbourne School of Population and Global Health, University of Melbourne, Bouverie Street, Carlton, 3053, Australia
| | - Deborah Cole
- Dental Health Services Victoria, 720 Swanston Street, Carlton, 3053, Australia
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Moss R, Zarebski A, Dawson P, McCaw JM. Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data. Influenza Other Respir Viruses 2016; 10:314-23. [PMID: 26859411 PMCID: PMC4910172 DOI: 10.1111/irv.12376] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/28/2016] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system. OBJECTIVES Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia). METHODS We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14. RESULTS AND CONCLUSIONS Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support.
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Affiliation(s)
- Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Alexander Zarebski
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Peter Dawson
- Land Personnel Protection Branch, Land Division, Defence Science and Technology Group, Melbourne, Australia
| | - James M McCaw
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.,Modelling & Simulation, Murdoch Childrens Research Institute, Royal Childrens Hospital, Melbourne, Australia
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Coudeville L, Baurin N, Vergu E. Estimation of parameters related to vaccine efficacy and dengue transmission from two large phase III studies. Vaccine 2015; 34:6417-6425. [PMID: 26614588 DOI: 10.1016/j.vaccine.2015.11.023] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 10/26/2015] [Accepted: 11/09/2015] [Indexed: 02/02/2023]
Abstract
BACKGROUND A tetravalent dengue vaccine was shown to be efficacious against symptomatic dengue in two phase III efficacy studies performed in five Asian and five Latin American countries. The objective here was to estimate key parameters of a dengue transmission model using the data collected during these studies. METHODS Parameter estimation was based on a Sequential Monte Carlo approach and used a cohort version of the transmission model. Serotype-specific basic reproduction numbers were derived for each country. Parameters related to serotype interactions included duration of cross-protection and level of cross-enhancement characterized by differences in symptomaticity for primary, secondary and post-secondary infections. We tested several vaccine efficacy profiles and simulated the evolution of vaccine efficacy over time for the scenarios providing the best fit to the data. RESULTS Two reference scenarios were identified. The first included temporary cross-protection and the second combined cross-protection and cross-enhancement upon wild-type infection and following vaccination. Both scenarios were associated with differences in efficacy by serotype, higher efficacy for pre-exposed subjects and against severe dengue, increase in efficacy with doses for naïve subjects and by a more important waning of vaccine protection for subjects when naïve than when pre-exposed. Over 20 years, the median reduction of dengue risk induced by the direct protection conferred by the vaccine ranged from 24% to 47% according to country for the first scenario and from 34% to 54% for the second. CONCLUSION Our study is an important first step in deriving a general framework that combines disease dynamics and mechanisms of vaccine protection that could be used to assess the impact of vaccination at a population level.
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Affiliation(s)
| | - Nicolas Baurin
- Vaccination Value Modeling, Sanofi Pasteur, Lyon, France
| | - Elisabeta Vergu
- MaIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France
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45
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Childs LM, Abuelezam NN, Dye C, Gupta S, Murray MB, Williams BG, Buckee CO. Modelling challenges in context: lessons from malaria, HIV, and tuberculosis. Epidemics 2015; 10:102-7. [PMID: 25843394 PMCID: PMC4451070 DOI: 10.1016/j.epidem.2015.02.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 02/09/2015] [Accepted: 02/09/2015] [Indexed: 02/08/2023] Open
Abstract
Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts.
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Affiliation(s)
- Lauren M Childs
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Nadia N Abuelezam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Christopher Dye
- Office of the Director General, World Health Organization, Avenue Appia, 1211 Geneva 27, Switzerland
| | - Sunetra Gupta
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Megan B Murray
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Division of Global Health Equity, Brigham & Women's Hospital, Boston, MA 02115, United States
| | - Brian G Williams
- South African Centre for Epidemiological Modelling and Analysis, Stellenbosch, South Africa; Wits Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
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