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Fisman DN. When the role of uncertainty is…uncertain. Proc Natl Acad Sci U S A 2023; 120:e2305856120. [PMID: 37224188 PMCID: PMC10265980 DOI: 10.1073/pnas.2305856120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023] Open
Affiliation(s)
- David N. Fisman
- Dalla Lana School of Public Health, University of Toronto, TorontoM5T 3M7, ON, Canada
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2
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Cascante-Vega J, Cordovez JM, Santos-Vega M. Estimating and forecasting the burden and spread of Colombia's SARS-CoV2 first wave. Sci Rep 2022; 12:13568. [PMID: 35945249 PMCID: PMC9427755 DOI: 10.1038/s41598-022-15514-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/24/2022] [Indexed: 12/03/2022] Open
Abstract
Following the rapid dissemination of COVID-19 cases in Colombia in 2020, large-scale non-pharmaceutical interventions (NPIs) were implemented as national emergencies in most of the country’s municipalities, starting with a lockdown on March 20th, 2020. Recently, approaches that combine movement data (measured as the number of commuters between units), metapopulation models to describe disease dynamics subdividing the population into Susceptible-Exposed-Asymptomatic-Infected-Recovered-Diseased and statistical inference algorithms have been pointed as a practical approach to both nowcast and forecast the number of cases and deaths. We used an iterated filtering (IF) framework to estimate the model transmission parameters using the reported data across 281 municipalities from March to late October in locations with more than 50 reported deaths and cases in Colombia. Since the model is high dimensional (6 state variables in every municipality), inference on those parameters is highly non-trivial, so we used an Ensemble-Adjustment-Kalman-Filter (EAKF) to estimate time variable system states and parameters. Our results show the model’s ability to capture the characteristics of the outbreak in the country and provide estimates of the epidemiological parameters in time at the national level. Importantly, these estimates could become the base for planning future interventions as well as evaluating the impact of NPIs on the effective reproduction number (\documentclass[12pt]{minimal}
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\begin{document}$$\mathscr {R}_{eff}$$\end{document}Reff) and the critical epidemiological parameters, such as the contact rate or the reporting rate. However, our forecast presents some inconsistency as it overestimates the deaths for some locations as Medellín. Nevertheless, our approach demonstrates that real-time, publicly available ensemble forecasts can provide short-term predictions of reported COVID-19 deaths in Colombia. Therefore, this model can be used as a forecasting tool to evaluate disease dynamics and aid policymakers in infectious outbreak management and control.
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Affiliation(s)
- Jaime Cascante-Vega
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia.,Facultad de Medicina, Universidad de los Andes, Bogotá D.C., Colombia
| | - Juan Manuel Cordovez
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia
| | - Mauricio Santos-Vega
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia. .,Facultad de Medicina, Universidad de los Andes, Bogotá D.C., Colombia.
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3
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Kelly JD, Park J, Harrigan RJ, Hoff NA, Lee SD, Wannier R, Selo B, Mossoko M, Njoloko B, Okitolonda-Wemakoy E, Mbala-Kingebeni P, Rutherford GW, Smith TB, Ahuka-Mundeke S, Muyembe-Tamfum JJ, Rimoin AW, Schoenberg FP. Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models. Epidemics 2019; 28:100354. [PMID: 31395373 PMCID: PMC7358183 DOI: 10.1016/j.epidem.2019.100354] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 07/11/2019] [Accepted: 07/11/2019] [Indexed: 12/17/2022] Open
Abstract
As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.
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Affiliation(s)
- J Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; F.I. Proctor Foundation, University of California, San Francisco, CA USA.
| | - Junhyung Park
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Ryan J Harrigan
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA
| | - Nicole A Hoff
- Department of Epidemiology, University of California, Los Angeles, CA, USA
| | - Sarita D Lee
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Rae Wannier
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | | | | | | | | | - George W Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Thomas B Smith
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA
| | | | | | - Anne W Rimoin
- Department of Epidemiology, University of California, Los Angeles, CA, USA
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4
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Li SL, Ferrari MJ, Bjørnstad ON, Runge MC, Fonnesbeck CJ, Tildesley MJ, Pannell D, Shea K. Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study. Proc Biol Sci 2019; 286:20190774. [PMID: 31213182 PMCID: PMC6599986 DOI: 10.1098/rspb.2019.0774] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.
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Affiliation(s)
- Shou-Li Li
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA.,2 State Key Laboratory of Grassland Agro-ecosystems, and College of Pastoral, Agriculture Science and Technology, Lanzhou University , People's Republic of China
| | - Matthew J Ferrari
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Ottar N Bjørnstad
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Michael C Runge
- 3 US Geological Survey, Patuxent Wildlife Research Center , Laurel, MD , USA
| | | | - Michael J Tildesley
- 5 Systems Biology and Infectious Disease Epidemiology Research Centre, School of Life Sciences and Mathematics Institute, University of Warwick , Coventry CV4 7AL , UK
| | - David Pannell
- 6 School of Agriculture and Environment, The University of Western Australia (M087) , Crawley, WA 6009 , Australia
| | - Katriona Shea
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
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Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building. Epidemics 2019; 26:116-127. [PMID: 30446431 PMCID: PMC7105018 DOI: 10.1016/j.epidem.2018.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/06/2018] [Accepted: 10/17/2018] [Indexed: 12/24/2022] Open
Abstract
Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
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Kraemer MUG, Cummings DAT, Funk S, Reiner RC, Faria NR, Pybus OG, Cauchemez S. Reconstruction and prediction of viral disease epidemics. Epidemiol Infect 2018; 147:e34. [PMID: 30394230 PMCID: PMC6398585 DOI: 10.1017/s0950268818002881] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/24/2018] [Accepted: 09/21/2018] [Indexed: 01/29/2023] Open
Abstract
A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.
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Affiliation(s)
- M. U. G. Kraemer
- Harvard Medical School, Harvard University, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - D. A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - S. Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - R. C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - N. R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - O. G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - S. Cauchemez
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé, Paris, France
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