1
|
Judson SD, Dowdy DW. Modeling zoonotic and vector-borne viruses. Curr Opin Virol 2024; 67:101428. [PMID: 39047313 PMCID: PMC11292992 DOI: 10.1016/j.coviro.2024.101428] [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: 02/02/2024] [Accepted: 07/06/2024] [Indexed: 07/27/2024]
Abstract
The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.
Collapse
Affiliation(s)
- Seth D Judson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - David W Dowdy
- Division of Infectious Disease Epidemiology, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| |
Collapse
|
2
|
Bleichrodt A, Luo R, Kirpich A, Chowell G. Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240248. [PMID: 39076375 PMCID: PMC11285753 DOI: 10.1098/rsos.240248] [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: 02/14/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook's Prophet model, as well as the sub-epidemic wave and n-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, the n-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. The n-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.
Collapse
Affiliation(s)
- Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Alexander Kirpich
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
3
|
McAndrew T, Gibson GC, Braun D, Srivastava A, Brown K. Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data. Epidemics 2024; 47:100756. [PMID: 38452456 DOI: 10.1016/j.epidem.2024.100756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.
Collapse
Affiliation(s)
- Thomas McAndrew
- Department of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of America.
| | - Graham C Gibson
- Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United States of America
| | - David Braun
- Department of Psychology College of Arts and Science, Lehigh University, Bethlehem PA, United States of America
| | - Abhishek Srivastava
- P.C. Rossin College of Engineering & Applied Science, Lehigh University, Bethlehem PA, United States of America
| | - Kate Brown
- Department of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of America
| |
Collapse
|
4
|
Charniga K, Madewell ZJ, Masters NB, Asher J, Nakazawa Y, Spicknall IH. Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned. Epidemics 2024; 47:100755. [PMID: 38452454 DOI: 10.1016/j.epidem.2024.100755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/14/2024] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.
Collapse
Affiliation(s)
- Kelly Charniga
- Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, CDC, USA.
| | | | | | - Jason Asher
- Center for Forecasting and Outbreak Analytics, CDC, USA
| | - Yoshinori Nakazawa
- Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, CDC, USA
| | - Ian H Spicknall
- Division of Sexually Transmitted Disease Prevention, National Center for HIV, Viral Hepatitis, STD, & TB Prevention, CDC, USA
| |
Collapse
|
5
|
Arroyo-Esquivel J, Klausmeier CA, Litchman E. Using neural ordinary differential equations to predict complex ecological dynamics from population density data. J R Soc Interface 2024; 21:20230604. [PMID: 38745459 DOI: 10.1098/rsif.2023.0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/25/2024] [Indexed: 05/16/2024] Open
Abstract
Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data (Chen et al. 2018 Adv. Neural Inf. Process. Syst.). Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here, we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. We also find that untuned NODEs have a similar forecasting accuracy to untuned long-short term memory neural networks and both are outperformed in accuracy and precision by empirical dynamical modelling . However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy. We also discuss ways to improve the forecasting performance of NODEs. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.
Collapse
Affiliation(s)
| | - Christopher A Klausmeier
- Department of Global Ecology, Carnegie Institution for Science , Stanford, CA, USA
- W. K. Kellogg Biological Station, Michigan State University , Hickory Corners, MI, USA
- Program in Ecology and Evolutionary Biology, Michigan State University , East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University , East Lansing, MI, USA
- Department of Plant Biology, Michigan State University , East Lansing, MI, USA
| | - Elena Litchman
- Department of Global Ecology, Carnegie Institution for Science , Stanford, CA, USA
- W. K. Kellogg Biological Station, Michigan State University , Hickory Corners, MI, USA
- Program in Ecology and Evolutionary Biology, Michigan State University , East Lansing, MI, USA
- Department of Integrative Biology, Michigan State University , East Lansing, MI, USA
| |
Collapse
|
6
|
Lopez VK, Cramer EY, Pagano R, Drake JM, O’Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud I, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu TY, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, Johansson MA. Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput Biol 2024; 20:e1011200. [PMID: 38709852 PMCID: PMC11098513 DOI: 10.1371/journal.pcbi.1011200] [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: 05/30/2023] [Revised: 05/16/2024] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Abstract
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
Collapse
Affiliation(s)
- Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Estee Y. Cramer
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Robert Pagano
- Unaffiliated, Tucson, Arizona, United States of America
| | - John M. Drake
- University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- University of Georgia, Athens, Georgia, United States of America
| | - Madeline Adee
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Turgay Ayer
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Jagpreet Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ozden O. Dalgic
- Value Analytics Labs, Boston, Massachusetts, United States of America
| | - Mary A. Ladd
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Benjamin P. Linas
- Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Peter P. Mueller
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Xiao
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Aaron Gerding
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Tilmann Gneiting
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Yuxin Huang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Dasuni Jayawardena
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Abdul H. Kanji
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Khoa Le
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
| | - Jarad Niemi
- Iowa State University, Ames, Iowa, United States of America
| | - Evan L. Ray
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ariane Stark
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Yijin Wang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Nutcha Wattanachit
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Martha W. Zorn
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Sen Pei
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Teresa K. Yamana
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Samuel R. Tarasewicz
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Daniel J. Wilson
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Sid Baccam
- IEM, Bel Air, Maryland, United States of America
| | - Heidi Gurung
- IEM, Bel Air, Maryland, United States of America
| | - Steve Stage
- IEM, Baton Rouge, Louisiana, United States of America
| | | | - Lei Gao
- George Mason University, Fairfax, Virginia, United States of America
| | - Zhiling Gu
- Iowa State University, Ames, Iowa, United States of America
| | - Myungjin Kim
- Kyungpook National University, Bukgu, Daegu, Republic of Korea
| | - Xinyi Li
- Clemson University, Clemson, South Carolina, United States of America
| | - Guannan Wang
- College of William & Mary, Williamsburg, Virginia, United States of America
| | - Lily Wang
- George Mason University, Fairfax, Virginia, United States of America
| | - Yueying Wang
- Amazon, Seattle, Washington, United States of America
| | - Shan Yu
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Lauren Gardner
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sonia Jindal
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Kristen Nixon
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L. Hill
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Elizabeth C. Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | | | - Justin Lessler
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Katharine Tallaksen
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Dean Karlen
- University of Victoria and TRIUMF, Victoria, British Columbia, Canada
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jiang Bian
- Microsoft, Redmond, Washington, United States of America
| | - Wei Cao
- Microsoft, Redmond, Washington, United States of America
| | - Zhifeng Gao
- Microsoft, Redmond, Washington, United States of America
| | | | - Chaozhuo Li
- Microsoft, Redmond, Washington, United States of America
| | - Tie-Yan Liu
- Microsoft, Redmond, Washington, United States of America
| | - Xing Xie
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zhang
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zheng
- Microsoft, Redmond, Washington, United States of America
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Jinghui Chen
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Quanquan Gu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lingxiao Wang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Pan Xu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Weitong Zhang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Difan Zou
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Graham Casey Gibson
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Sheldon
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Gursharn Kaur
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | | | | | - Lijing Wang
- New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Pragati V. Prasad
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alexander E. Webber
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Nicholas G. Reich
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| |
Collapse
|
7
|
d'Andrea V, Trentini F, Marziano V, Zardini A, Manica M, Guzzetta G, Ajelli M, Petrone D, Del Manso M, Sacco C, Andrianou X, Bella A, Riccardo F, Pezzotti P, Poletti P, Merler S. Spatial spread of COVID-19 during the early pandemic phase in Italy. BMC Infect Dis 2024; 24:450. [PMID: 38684947 PMCID: PMC11057115 DOI: 10.1186/s12879-024-09343-8] [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: 07/19/2023] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.
Collapse
Affiliation(s)
- Valeria d'Andrea
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
- Department of Physics and Astronomy "Galileo Galilei", University of Padua, Padua, Italy
| | - Filippo Trentini
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
- Dondena Centre for Research On Social Dynamics and Public Policy, Bocconi University, Milan, Italy
- Department of Decision Sciences, Bocconi University, Milan, Italy
| | | | - Agnese Zardini
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Mattia Manica
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Daniele Petrone
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
- Department of Statistics, Sapienza University of Rome, Rome, Italy
| | - Martina Del Manso
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Chiara Sacco
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Xanthi Andrianou
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Antonino Bella
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Flavia Riccardo
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Piero Poletti
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy
| | - Stefano Merler
- Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
| |
Collapse
|
8
|
Srivathsan A, Abdou A, Al-Khatib T, Apadinuwe SC, Badiane MD, Bucumi V, Chisenga T, Kabona G, Kabore M, Kanyi SK, Bella L, M’po N, Masika M, Minnih A, Sitoe HM, Mishra S, Olobio N, Omar FJ, Phiri I, Sanha S, Seife F, Sharma S, Tekeraoi R, Traore L, Watitu T, Bol YY, Borlase A, Deiner MS, Renneker KK, Hooper PJ, Emerson PM, Vasconcelos A, Arnold BF, Porco TC, Hollingsworth TD, Lietman TM, Blumberg S. District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach. Clin Infect Dis 2024; 78:S101-S107. [PMID: 38662700 PMCID: PMC11045026 DOI: 10.1093/cid/ciae031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Assessing the feasibility of 2030 as a target date for global elimination of trachoma, and identification of districts that may require enhanced treatment to meet World Health Organization (WHO) elimination criteria by this date are key challenges in operational planning for trachoma programmes. Here we address these challenges by prospectively evaluating forecasting models of trachomatous inflammation-follicular (TF) prevalence, leveraging ensemble-based approaches. Seven candidate probabilistic models were developed to forecast district-wise TF prevalence in 11 760 districts, trained using district-level data on the population prevalence of TF in children aged 1-9 years from 2004 to 2022. Geographical location, history of mass drug administration treatment, and previously measured prevalence data were included in these models as key predictors. The best-performing models were included in an ensemble, using weights derived from their relative likelihood scores. To incorporate the inherent stochasticity of disease transmission and challenges of population-level surveillance, we forecasted probability distributions for the TF prevalence in each geographic district, rather than predicting a single value. Based on our probabilistic forecasts, 1.46% (95% confidence interval [CI]: 1.43-1.48%) of all districts in trachoma-endemic countries, equivalent to 172 districts, will exceed the 5% TF control threshold in 2030 with the current interventions. Global elimination of trachoma as a public health problem by 2030 may require enhanced intervention and/or surveillance of high-risk districts.
Collapse
Affiliation(s)
- Ariktha Srivathsan
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Amza Abdou
- Programme National de Santé Oculaire, Ministère De La Santé Publique, Niamey, Niger
| | - Tawfik Al-Khatib
- Prevention of Blindness Program, Ministry of Public Health & Population, Sana'a, Yemen
| | | | - Mouctar D Badiane
- Programme National de Promotion de La Santé Oculaire, Ministère de la Santé et de L'Action sociale, Dakar, Sénégal
| | - Victor Bucumi
- Département En Charge des Maladies Tropicales Négligées, Ministère De La Santé Publique Et De La Lutte Contre Le Sida, Bujumbura, Burundi
| | - Tina Chisenga
- Ministry of Health Public Health Department, Lusaka, Zambia
| | - George Kabona
- Neglected Tropical Disease Control Program, Ministry of Health and Social Welfare, Dar Es Salaam, United Republic of Tanzania
| | - Martin Kabore
- Programme national de lutte contre les maladies tropicales négligées, Ministère de la santé et de l'hygiène publique, Ouagadougou, Burkina Faso
| | - Sarjo Kebba Kanyi
- The National Eye Health Programme, Ministry of Health and Social Welfare, Banjul, Kanifing, The Gambia
| | - Lucienne Bella
- Programme National De Lutte Contre La Cécité, Ministère De La Santé Publique, Yaoundé, Cameroon
| | - Nekoua M’po
- Programme National De Lutte Contre Les Maladies Transmissibles, Ministère De La Santé, Cotonou, Benin
| | - Michael Masika
- Department of Clinical Services, Ministry of Health, Lilongwe, Malawi
| | - Abdellahi Minnih
- Département Des Maladies Transmissibles, Ministère De La Santé Nouakchott, Nouakchott, Mauritania
| | - Henis Mior Sitoe
- Direcção Nacional De Saúde Pública Ministerio Da Saude, Maputo, Mozambique
| | | | - Nicholas Olobio
- National Trachoma Elimination Programme, Federal Ministry of Health, Abuja, Nigeria
| | | | - Isaac Phiri
- Department of Epidemiology and Disease Control, Ministry of Health & Child Welfare, Harare, Zimbabwe
| | - Salimato Sanha
- Programa Nacional De Saúde De Visão, Minsap, Bissau, Guinea-Bissau
| | - Fikre Seife
- Federal Ministry of Health, Addis Ababa, Ethiopia
| | | | - Rabebe Tekeraoi
- Eye Department, Ministry of Health and Medical Services, South Tarawa, Kiribati
| | - Lamine Traore
- Programme National de la Santé Oculaire, Ministère de la Santé, Bamako, Mali
| | | | - Yak Yak Bol
- Neglected Tropical Diseases Programme, Ministry of Health, Juba, South Sudan
| | - Anna Borlase
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Michael S Deiner
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Kristen K Renneker
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - P J Hooper
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Paul M Emerson
- International Trachoma Initiative, The Task Force for Global Health, Decatur, Georgia, USA
| | - Andreia Vasconcelos
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Benjamin F Arnold
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Travis C Porco
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Thomas M Lietman
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, California, USA
- Department of Medicine, University of California, San Francisco, California, USA
| |
Collapse
|
9
|
Zhou G, Lee MC, Wang X, Zhong D, Githeko AK, Yan G. Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches. Am J Trop Med Hyg 2024; 110:421-430. [PMID: 38350135 PMCID: PMC10919169 DOI: 10.4269/ajtmh.23-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/03/2023] [Indexed: 02/15/2024] Open
Abstract
Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.
Collapse
Affiliation(s)
- Guofa Zhou
- Program in Public Health, University of California, Irvine, California
| | - Ming-Chieh Lee
- Program in Public Health, University of California, Irvine, California
| | - Xiaoming Wang
- Program in Public Health, University of California, Irvine, California
| | - Daibin Zhong
- Program in Public Health, University of California, Irvine, California
| | - Andrew K. Githeko
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Guiyun Yan
- Program in Public Health, University of California, Irvine, California
| |
Collapse
|
10
|
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 .
Collapse
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
| |
Collapse
|
11
|
Bleichrodt A, Luo R, Kirpich A, Chowell G. Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14 th, 2022, through February 26th, 2023. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.15.23289989. [PMID: 37905035 PMCID: PMC10615009 DOI: 10.1101/2023.05.15.23289989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and n -sub-epidemic modeling frameworks. We assess forecast performance using average mean squared error (MSE), mean absolute error (MAE), weighted interval score (WIS), 95% prediction interval coverage (95% PI coverage), and skill scores. Average Winkler scores were used to calculate skill scores for 95% PI coverage. Overall, the n -sub-epidemic modeling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best across all forecasting horizons for most locations regarding average MSE, MAE, WIS, and 95% PI coverage. However, many locations had multiple models performing equally well for the average 95% PI coverage. The n -sub-epidemic and spatial-wave frameworks improved considerably in average MSE, MAE, and WIS, and Winkler scores (95% PI coverage) relative to the ARIMA model. Findings lend further support to sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.
Collapse
|
12
|
Panaggio MJ, Wilson SN, Ratcliff JD, Mullany LC, Freeman JD, Rainwater-Lovett K. On the Mark: Modeling and Forecasting for Public Health Impact. Health Secur 2023; 21:S79-S88. [PMID: 37756211 DOI: 10.1089/hs.2023.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023] Open
Affiliation(s)
- Mark J Panaggio
- Mark J. Panaggio, PhD, is Applied Mathematicians/Data Scientists, Johns Hopkins University Applied Physics
| | - Shelby N Wilson
- Shelby N. Wilson, PhD, is Applied Mathematicians/Data Scientists, Johns Hopkins University Applied Physics
| | - Jeremy D Ratcliff
- Jeremy D. Ratcliff, PhD, is a Senior Scientist, Asymmetric Operations Sector, Johns Hopkins University Applied Physics
| | - Luke C Mullany
- Luke C. Mullany, PhD, MS, MHS, is a Senior Researcher, Research and Exploratory Development Department, Johns Hopkins University Applied Physics
| | - Jeffrey D Freeman
- Jeffrey D. Freeman, PhD, MPH, is Director and Special Assistant to the President, National Center for Disaster Medicine and Public Health, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Kaitlin Rainwater-Lovett
- Kaitlin Rainwater-Lovett, PhD, MPH, is Assistant Program Manager, Johns Hopkins University Applied Physics Laboratory, Laurel, MD
| |
Collapse
|
13
|
Gibbs H, Musah A, Seidu O, Ampofo W, Asiedu-Bekoe F, Gray J, Adewole WA, Cheshire J, Marks M, Eggo RM. Call detail record aggregation methodology impacts infectious disease models informed by human mobility. PLoS Comput Biol 2023; 19:e1011368. [PMID: 37561812 PMCID: PMC10443843 DOI: 10.1371/journal.pcbi.1011368] [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: 01/25/2023] [Revised: 08/22/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
Collapse
Affiliation(s)
- Hamish Gibbs
- Department of Geography, University College London, London, United Kingdom
| | - Anwar Musah
- Department of Geography, University College London, London, United Kingdom
| | | | - William Ampofo
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | | | | | | | - James Cheshire
- Department of Geography, University College London, London, United Kingdom
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospital, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
14
|
Linking mathematical models and trap data to infer the proliferation, abundance, and control of Aedes aegypti. Acta Trop 2023; 239:106837. [PMID: 36657506 DOI: 10.1016/j.actatropica.2023.106837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/05/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023]
Abstract
Aedes aegypti is one of the most dominant mosquito species in the urban areas of Miami-Dade County, Florida, and is responsible for the local arbovirus transmissions. Since August 2016, mosquito traps have been placed throughout the county to improve surveillance and guide mosquito control and arbovirus outbreak response. In this paper, we develop a deterministic mosquito population model, estimate model parameters by using local entomological and temperature data, and use the model to calibrate the mosquito trap data from 2017 to 2019. We further use the model to compare the Ae. aegypti population and evaluate the impact of rainfall intensity in different urban built environments. Our results show that rainfall affects the breeding sites and the abundance of Ae. aegypti more significantly in tourist areas than in residential places. In addition, we apply the model to quantitatively assess the effectiveness of vector control strategies in Miami-Dade County.
Collapse
|
15
|
Howerton E, Runge MC, Bogich TL, Borchering RK, Inamine H, Lessler J, Mullany LC, Probert WJM, Smith CP, Truelove S, Viboud C, Shea K. Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology. J R Soc Interface 2023; 20:20220659. [PMID: 36695018 PMCID: PMC9874266 DOI: 10.1098/rsif.2022.0659] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/03/2023] [Indexed: 01/26/2023] Open
Abstract
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
Collapse
Affiliation(s)
- Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Michael C. Runge
- Eastern Ecological Science Center at the Patuxent Research Refuge, U.S. Geological Survey, Laurel, MD, USA
| | - Tiffany L. Bogich
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Rebecca K. Borchering
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Hidetoshi Inamine
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology and Carolina Population Center, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Luke C. Mullany
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, USA
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Claire P. Smith
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Shaun Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| |
Collapse
|
16
|
Perrotta D, Frias-Martinez E, Pastore y Piontti A, Zhang Q, Luengo-Oroz M, Paolotti D, Tizzoni M, Vespignani A. Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia. PLoS Negl Trop Dis 2022; 16:e0010565. [PMID: 35857744 PMCID: PMC9299334 DOI: 10.1371/journal.pntd.0010565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 06/06/2022] [Indexed: 11/19/2022] Open
Abstract
Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.
Collapse
Affiliation(s)
- Daniela Perrotta
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
- * E-mail:
| | | | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, New York, State of New York, United States of America
| | | | | | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| |
Collapse
|
17
|
Paireau J, Andronico A, Hozé N, Layan M, Crépey P, Roumagnac A, Lavielle M, Boëlle PY, Cauchemez S. An ensemble model based on early predictors to forecast COVID-19 health care demand in France. Proc Natl Acad Sci U S A 2022; 119:e2103302119. [PMID: 35476520 PMCID: PMC9170016 DOI: 10.1073/pnas.2103302119] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/23/2022] [Indexed: 12/24/2022] Open
Abstract
Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 health care demand in hospitals. Here, we evaluate the performance of 12 individual models and 19 predictors to anticipate French COVID-19-related health care needs from September 7, 2020, to March 6, 2021. We then build an ensemble model by combining the individual forecasts and retrospectively test this model from March 7, 2021, to July 6, 2021. We find that the inclusion of early predictors (epidemiological, mobility, and meteorological predictors) can halve the rms error for 14-d–ahead forecasts, with epidemiological and mobility predictors contributing the most to the improvement. On average, the ensemble model is the best or second-best model, depending on the evaluation metric. Our approach facilitates the comparison and benchmarking of competing models through their integration in a coherent analytical framework, ensuring that avenues for future improvements can be identified.
Collapse
Affiliation(s)
- Juliette Paireau
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France
- Direction des Maladies Infectieuses, Santé publique France, 94415 Saint Maurice, France
| | - Alessio Andronico
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France
| | - Nathanaël Hozé
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France
| | - Pascal Crépey
- Arènes–UMR 6051, RSMS–U 1309, Ecole des Hautes Etudes en Santé Publique, INSERM, CNRS, Université de Rennes, 35043 Rennes, France
| | | | - Marc Lavielle
- INRIA, 91120 Palaiseau, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Pierre-Yves Boëlle
- Institut Pierre Louis d’Epidémiologie et de Santé Publique, INSERM, Sorbonne Université, 75012 Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, 75015 Paris, France
| |
Collapse
|