1
|
Hamilton A, Haghpanah F, Tulchinsky A, Kipshidze N, Poleon S, Lin G, Du H, Gardner L, Klein E. Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review. DIALOGUES IN HEALTH 2024; 4:100179. [PMID: 38813579 PMCID: PMC11134564 DOI: 10.1016/j.dialog.2024.100179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 05/04/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
Background During the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation). Methods This review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread. Findings Of the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared versions of their model with and without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves. Interpretation While many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- or overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics. Funding This study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).
Collapse
Affiliation(s)
- Alisa Hamilton
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
- Department of Civil & Systems Engineering, Johns Hopkins University, 3400 North Charles St, Baltimore, MD 21218, USA
| | - Fardad Haghpanah
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
| | - Alexander Tulchinsky
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
| | - Nodar Kipshidze
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
| | - Suprena Poleon
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
| | - Gary Lin
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
- Johns Hopkins Applied Physics Laboratory, 1110 Johns Hopkins Rd, Laurel, MD 20723, USA
| | - Hongru Du
- Department of Civil & Systems Engineering, Johns Hopkins University, 3400 North Charles St, Baltimore, MD 21218, USA
| | - Lauren Gardner
- Department of Civil & Systems Engineering, Johns Hopkins University, 3400 North Charles St, Baltimore, MD 21218, USA
| | - Eili Klein
- One Health Trust, 5636 Connecticut Avenue NW, PO Box 4235, Washington, DC 20015, USA
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21209, USA
- Department of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| |
Collapse
|
2
|
Zhou L, Wang L, Liu G, Cai E. Prognosis prediction models for post-stroke depression: a protocol for systematic review, meta-analysis, and critical appraisal. Syst Rev 2024; 13:138. [PMID: 38778417 PMCID: PMC11110183 DOI: 10.1186/s13643-024-02544-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION Post-stroke depression (PSD) is a prevalent complication that has been shown to have a negative impact on rehabilitation outcomes and quality of life and poses a significant risk for suicidal intention. However, models for discriminating and predicting PSD in stroke survivors for effective secondary prevention strategies are inadequate as the pathogenesis of PSD remains unknown. Prognostic prediction models that exhibit greater rule-in capacity have the potential to mitigate the issue of underdiagnosis and undertreatment of PSD. Thus, the planned study aims to systematically review and critically evaluate published studies on prognostic prediction models for PSD. METHODS AND ANALYSIS A systematic literature search will be conducted in PubMed and Embase through Ovid. Two reviewers will complete study screening, data extraction, and quality assessment utilizing appropriate tools. Qualitative data on the characteristics of the included studies, methodological quality, and the appraisal of the clinical applicability of models will be summarized in the form of narrative comments and tables or figures. The predictive performance of the same model involving multiple studies will be synthesized with a random effects meta-analysis model or meta-regression, taking into account heterogeneity. ETHICS AND DISSEMINATION Ethical approval is considered not applicable for this systematic review. Findings will be shared through dissemination at academic conferences and/or publication in peer-reviewed academic journals. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42023388548.
Collapse
Affiliation(s)
- Lu Zhou
- School of Nursing, Yunnan University of Chinese Medicine, Kunming, China
| | - Lei Wang
- School of Nursing, Yunnan University of Chinese Medicine, Kunming, China
| | - Gao Liu
- School of Nursing, Yunnan University of Chinese Medicine, Kunming, China
| | - EnLi Cai
- School of Nursing, Yunnan University of Chinese Medicine, Kunming, China.
| |
Collapse
|
3
|
Jit M, Cook AR. Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation. Annu Rev Public Health 2024; 45:133-150. [PMID: 37871140 DOI: 10.1146/annurev-publhealth-060222-025149] [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] [Indexed: 10/25/2023]
Abstract
Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.
Collapse
Affiliation(s)
- Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom;
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- National University Health System, Singapore
| |
Collapse
|
4
|
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
|
5
|
Zamarreño JM, Torres-Franco AF, Gonçalves J, Muñoz R, Rodríguez E, Eiros JM, García-Encina P. Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170367. [PMID: 38278261 DOI: 10.1016/j.scitotenv.2024.170367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 01/28/2024]
Abstract
Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.
Collapse
Affiliation(s)
- Jesús M Zamarreño
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of System Engineering and Automatic Control, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina s/n, 47011 Valladolid, Spain.
| | - Andrés F Torres-Franco
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain.
| | - José Gonçalves
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - Raúl Muñoz
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - Elisa Rodríguez
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - José María Eiros
- Microbiology Service, Hospital Universitario Río Hortega, Gerencia Regional de Salud, Paseo de Zorrilla 1, 47007 Valladolid, Spain
| | - Pedro García-Encina
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| |
Collapse
|
6
|
de Wit MM, Dimas Martins A, Delecroix C, Heesterbeek H, ten Bosch QA. Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization. Proc Biol Sci 2024; 291:20232432. [PMID: 38471554 PMCID: PMC10932716 DOI: 10.1098/rspb.2023.2432] [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: 10/30/2023] [Accepted: 02/14/2024] [Indexed: 03/14/2024] Open
Abstract
Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.
Collapse
Affiliation(s)
- Mariken M. de Wit
- Quantitative Veterinary Epidemiology, Wageningen University and Research, Wageningen, The Netherlands
| | - Afonso Dimas Martins
- Department of Population Health Sciences, Faculty of Veterinary Medicine, University of Utrecht, Utrecht, The Netherlands
| | - Clara Delecroix
- Quantitative Veterinary Epidemiology, Wageningen University and Research, Wageningen, The Netherlands
- Department of Environmental Sciences, Wageningen University and Research, Wageningen, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Faculty of Veterinary Medicine, University of Utrecht, Utrecht, The Netherlands
| | - Quirine A. ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University and Research, Wageningen, The Netherlands
| |
Collapse
|
7
|
Henderson AS, Hickson RI, Furlong M, McBryde ES, Meehan MT. Reproducibility of COVID-era infectious disease models. Epidemics 2024; 46:100743. [PMID: 38290265 DOI: 10.1016/j.epidem.2024.100743] [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: 10/11/2023] [Revised: 12/21/2023] [Accepted: 01/20/2024] [Indexed: 02/01/2024] Open
Abstract
Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus' transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.
Collapse
Affiliation(s)
- Alec S Henderson
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia.
| | - Roslyn I Hickson
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia; Commonwealth Scientific Industrial Research Organisation (CSIRO), Townsville, Australia
| | - Morgan Furlong
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
| | - Emma S McBryde
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
| | - Michael T Meehan
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia
| |
Collapse
|
8
|
Singh MP, Rajvanshi H, Bharti PK, Anvikar AR, Lal AA. Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting malaria in India towards the 2030 elimination goals. Malar J 2024; 23:50. [PMID: 38360708 PMCID: PMC10870538 DOI: 10.1186/s12936-024-04872-8] [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: 12/21/2023] [Accepted: 02/06/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Despite the progress made in this decade towards malaria elimination, it remains a significant public health concern in India and many other countries in South Asia and Asia Pacific region. Understanding the historical trends of malaria incidence in relation to various commodity and policy interventions and identifying the factors associated with its occurrence can inform future intervention strategies for malaria elimination goals. METHODS This study analysed historical malaria cases in India from 1990 to 2022 to assess the annual trends and the impact of key anti-malarial interventions on malaria incidence. Factors associated with malaria incidence were identified using univariate and multivariate linear regression analyses. Generalized linear, smoothing, autoregressive integrated moving averages (ARIMA) and Holt's models were used to forecast malaria cases from 2023 to 2030. RESULTS The reported annual malaria cases in India during 1990-2000 were 2.38 million, which dropped to 0.73 million cases annually during 2011-2022. The overall reduction from 1990 (2,018,783) to 2022 (176,522) was 91%. The key interventions of the Enhanced Malaria Control Project (EMCP), Intensified Malaria Control Project (IMCP), use of bivalent rapid diagnostic tests (RDT-Pf/Pv), artemisinin-based combination therapy (ACT), and involvement of the Accredited Social Health Activists (ASHAs) as front-line workers were found to result in the decline of malaria significantly. The ARIMA and Holt's models projected a continued decline in cases with the potential for reaching zero indigenous cases by 2027-2028. Important factors influencing malaria incidence included tribal population density, literacy rate, health infrastructure, and forested and hard-to-reach areas. CONCLUSIONS Studies aimed at assessing the impact of major commodity and policy interventions on the incidence of disease and studies of disease forecasting will inform programmes and policymakers of steps needed during the last mile phase to achieve malaria elimination. It is proposed that these time series and disease forecasting studies should be performed periodically using granular (monthly) and meteorological data to validate predictions of prior studies and suggest any changes needed for elimination efforts at national and sub-national levels.
Collapse
Affiliation(s)
- Mrigendra P Singh
- Foundation for Disease Elimination and Control of India, Mumbai, India
| | - Harsh Rajvanshi
- Foundation for Disease Elimination and Control of India, Mumbai, India
- Asia Pacific Leaders' Malaria Alliance, Singapore, Singapore
| | - Praveen K Bharti
- Indian Council of Medical Research-National Institute of Malaria Research, New Delhi, India
| | - Anup R Anvikar
- Indian Council of Medical Research-National Institute of Malaria Research, New Delhi, India
| | - Altaf A Lal
- Foundation for Disease Elimination and Control of India, Mumbai, India.
- Sun Pharmaceutical Industries Ltd, Mumbai, India.
- Global Health and Pharmaceuticals, Inc, Atlanta, GA, USA.
| |
Collapse
|
9
|
Harries M, Jaeger VK, Rodiah I, Hassenstein MJ, Ortmann J, Dreier M, von Holt I, Brinkmann M, Dulovic A, Gornyk D, Hovardovska O, Kuczewski C, Kurosinski MA, Schlotz M, Schneiderhan-Marra N, Strengert M, Krause G, Sester M, Klein F, Petersmann A, Karch A, Lange B. Bridging the gap - estimation of 2022/2023 SARS-CoV-2 healthcare burden in Germany based on multidimensional data from a rapid epidemic panel. Int J Infect Dis 2024; 139:50-58. [PMID: 38008353 DOI: 10.1016/j.ijid.2023.11.014] [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: 06/26/2023] [Revised: 11/09/2023] [Accepted: 11/10/2023] [Indexed: 11/28/2023] Open
Abstract
OBJECTIVES Throughout the SARS-CoV-2 pandemic, Germany like other countries lacked adaptive population-based panels to monitor the spread of epidemic diseases. METHODS To fill a gap in population-based estimates needed for winter 2022/23 we resampled in the German SARS-CoV-2 cohort study MuSPAD in mid-2022, including characterization of systemic cellular and humoral immune responses by interferon-γ-release assay (IGRA) and CLIA/IVN assay. We were able to confirm categorization of our study population into four groups with differing protection levels against severe COVID-19 courses based on literature synthesis. Using these estimates, we assessed potential healthcare burden for winter 2022/23 in different scenarios with varying assumptions on transmissibility, pathogenicity, new variants, and vaccine booster campaigns in ordinary differential equation models. RESULTS We included 9921 participants from eight German regions. While 85% of individuals were located in one of the two highest protection categories, hospitalization estimates from scenario modeling were highly dependent on viral variant characteristics ranging from 30-300% compared to the 02/2021 peak. Our results were openly communicated and published to an epidemic panel network and a newly established modeling network. CONCLUSIONS We demonstrate feasibility of a rapid epidemic panel to provide complex immune protection levels for inclusion in dynamic disease burden modeling scenarios.
Collapse
Affiliation(s)
- Manuela Harries
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany; Institute for Epidemiology Social Medicine and Health Systems Research, Hannover Medical School (MHH) Hannover, Germany.
| | - Veronika K Jaeger
- Institute of Epidemiology and Social Medicine, University of Münster, Germany
| | - Isti Rodiah
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Max J Hassenstein
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Julia Ortmann
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Maren Dreier
- Institute for Epidemiology Social Medicine and Health Systems Research, Hannover Medical School (MHH) Hannover, Germany
| | - Isabell von Holt
- Institute for Epidemiology Social Medicine and Health Systems Research, Hannover Medical School (MHH) Hannover, Germany
| | - Melanie Brinkmann
- Institute for Epidemiology Social Medicine and Health Systems Research, Hannover Medical School (MHH) Hannover, Germany
| | - Alex Dulovic
- NMI Natural and Medical Sciences, Institute at the University of Tubingen Reutlingen, Germany
| | - Daniela Gornyk
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Olga Hovardovska
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Christina Kuczewski
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | | | - Maike Schlotz
- Laboratory of Experimental Immunology, Institute of Virology Faculty of Medicine and University Hospital Cologne University of Cologne Cologne, Germany
| | | | - Monika Strengert
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Gérard Krause
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany; German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Martina Sester
- Department of transplant and infection immunology, Saarland University, Germany
| | - Florian Klein
- Laboratory of Experimental Immunology, Institute of Virology Faculty of Medicine and University Hospital Cologne University of Cologne Cologne, Germany; German Center for Infection Research, Partner site Bonn-Cologne Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne Cologne, Germany
| | - Astrid Petersmann
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald Greifswald, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Oldenburg Oldenburg, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Germany
| | - Berit Lange
- Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany; German Center for Infection Research (DZIF), Braunschweig, Germany
| |
Collapse
|
10
|
Reich NG, Wang Y, Burns M, Ergas R, Cramer EY, Ray EL. Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States. Epidemics 2023; 45:100728. [PMID: 37976681 PMCID: PMC10871058 DOI: 10.1016/j.epidem.2023.100728] [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: 03/24/2023] [Revised: 09/29/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.
Collapse
Affiliation(s)
- Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America.
| | - Yijin Wang
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Meagan Burns
- Massachusetts Department of Public Health, Boston, MA, United States of America
| | - Rosa Ergas
- Massachusetts Department of Public Health, Boston, MA, United States of America
| | - Estee Y Cramer
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Evan L Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
| |
Collapse
|
11
|
Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
Collapse
Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
12
|
Natalia YA, Delporte M, De Witte D, Beutels P, Dewatripont M, Molenberghs G. Assessing the impact of COVID-19 passes and mandates on disease transmission, vaccination intention, and uptake: a scoping review. BMC Public Health 2023; 23:2279. [PMID: 37978472 PMCID: PMC10656887 DOI: 10.1186/s12889-023-17203-4] [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: 06/07/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE Policymakers have struggled to maintain SARS-CoV-2 transmission at levels that are manageable to contain the COVID-19 disease burden while enabling a maximum of societal and economic activities. One of the tools that have been used to facilitate this is the so-called "COVID-19 pass". We aimed to document current evidence on the effectiveness of COVID-19 passes, distinguishing their indirect effects by improving vaccination intention and uptake from their direct effects on COVID-19 transmission measured by the incidence of cases, hospitalizations, and deaths. METHODS We performed a scoping review on the scientific literature of the proposed topic covering the period January 2021 to September 2022, in accordance with the PRISMA-ScR guidelines for scoping reviews. RESULTS Out of a yield of 4,693 publications, 45 studies from multiple countries were retained for full-text review. The results suggest that implementing COVID-19 passes tends to reduce the incidence of cases, hospitalizations, and deaths due to COVID-19. The use of COVID-19 passes was also shown to improve overall vaccination uptake and intention, but not in people who hold strong anti-COVID-19 vaccine beliefs. CONCLUSION The evidence from the literature we reviewed tends to indicate positive direct and indirect effects from the use of COVID-19 passes. A major limitation to establishing this firmly is the entanglement of individual effects of multiple measures being implemented simultaneously.
Collapse
Affiliation(s)
| | - Margaux Delporte
- I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Dries De Witte
- I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Mathias Dewatripont
- I3h, ECARES and Solvay Brussels School of Economics and Management, Université Libre de Bruxelles, Brussels, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| |
Collapse
|
13
|
Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, Venkatramanan S, Lowe R, Porco T, Berry IM, Deshpande A, Kraemer MUG, Blazes DL, Pan-Ngum W, Vespigiani A, Mate SE, Silal SP, Kandula S, Sippy R, Quandelacy TM, Morgan JJ, Ball J, Morton LC, Althouse BM, Pavlin J, van Panhuis W, Riley S, Biggerstaff M, Viboud C, Brady O, Rivers C. Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines. PLoS Med 2023; 20:e1004316. [PMID: 37976465 PMCID: PMC10656116 DOI: 10.1371/journal.pmed.1004316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pmed.1003793.].
Collapse
|
14
|
Pokutnaya D, Van Panhuis WG, Childers B, Hawkins MS, Arcury-Quandt AE, Matlack M, Carpio K, Hochheiser H. Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research. BMC Infect Dis 2023; 23:733. [PMID: 37891462 PMCID: PMC10612332 DOI: 10.1186/s12879-023-08729-4] [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: 03/22/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. METHODS Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. RESULTS Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. CONCLUSIONS The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.
Collapse
Affiliation(s)
- Darya Pokutnaya
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States of America.
| | - Willem G Van Panhuis
- Office of Data Science and Emerging Technologies, National Institute of Allergy and Infectious Diseases, Rockville, MD, United States of America
| | - Bruce Childers
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Marquis S Hawkins
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Alice E Arcury-Quandt
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Meghan Matlack
- Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kharlya Carpio
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Harry Hochheiser
- Department of Biomedical Informatics, Intelligent Systems Program, and Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, United States of America
| |
Collapse
|
15
|
Lim AY, Jafari Y, Caldwell JM, Clapham HE, Gaythorpe KAM, Hussain-Alkhateeb L, Johansson MA, Kraemer MUG, Maude RJ, McCormack CP, Messina JP, Mordecai EA, Rabe IB, Reiner RC, Ryan SJ, Salje H, Semenza JC, Rojas DP, Brady OJ. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk. BMC Infect Dis 2023; 23:708. [PMID: 37864153 PMCID: PMC10588093 DOI: 10.1186/s12879-023-08717-8] [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: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
Collapse
Affiliation(s)
- Ah-Young Lim
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Yalda Jafari
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jamie M Caldwell
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Laith Hussain-Alkhateeb
- School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden
- Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Michael A Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA
| | | | - Richard J Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clare P McCormack
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Ingrid B Rabe
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sadie J Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jan C Semenza
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden
| | - Diana P Rojas
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| |
Collapse
|
16
|
Wang J, Wang C. The coming Omicron waves and factors affecting its spread after China reopening borders. BMC Med Inform Decis Mak 2023; 23:186. [PMID: 37715187 PMCID: PMC10503199 DOI: 10.1186/s12911-023-02219-y] [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: 03/03/2023] [Accepted: 06/27/2023] [Indexed: 09/17/2023] Open
Abstract
The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model [Formula: see text], and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified [Formula: see text] model. The results demonstrate that the second and third waves will occur in July-September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders.
Collapse
Affiliation(s)
- Jixiao Wang
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, 6009, Australia.
| | - Chong Wang
- School of Business, Nanjing Audit University, Nanjing, 211815, China
| |
Collapse
|
17
|
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
|
18
|
Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291998. [PMID: 37461674 PMCID: PMC10350156 DOI: 10.1101/2023.06.28.23291998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
Collapse
Affiliation(s)
| | | | | | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center (NIH)
| | | | | | - Sara L Loo
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Ma S, Ning S, Yang S. Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information. COMMUNICATIONS MEDICINE 2023; 3:39. [PMID: 36964311 PMCID: PMC10038385 DOI: 10.1038/s43856-023-00272-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases. METHODS Inspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals. RESULTS In the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals). CONCLUSIONS The success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.
Collapse
Affiliation(s)
- Simin Ma
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shaoyang Ning
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, 01267, USA
| | - Shihao Yang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
20
|
Pokutnaya D, Van Panhuis WG, Childers B, Hawkins MS, Arcury-Quandt AE, Matlack M, Carpio K, Hochheiser H. Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.21.23287529. [PMID: 36993426 PMCID: PMC10055605 DOI: 10.1101/2023.03.21.23287529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Background Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.
Collapse
Affiliation(s)
- Darya Pokutnaya
- University of Pittsburgh, Department of Epidemiology; Pittsburgh, Pennsylvania, United States of America
| | - Willem G Van Panhuis
- Office of Data Science and Emerging Technologies, National Institute of Allergy and Infectious Diseases; Rockville, Maryland, United States of America [note that Dr. Van Panhuis completed the research described in this paper during his time at the University of Pittsburgh, before starting his position at NIAID]
| | - Bruce Childers
- University of Pittsburgh, Department of Computer Science; Pittsburgh, Pennsylvania, United States of America
| | - Marquis S Hawkins
- University of Pittsburgh, Department of Epidemiology; Pittsburgh, Pennsylvania, United States of America
| | - Alice E Arcury-Quandt
- University of Pittsburgh, Department of Epidemiology; Pittsburgh, Pennsylvania, United States of America
| | - Meghan Matlack
- University of Pittsburgh, Department of Environmental and Occupational Health, Pittsburgh, PA, USA
| | - Kharlya Carpio
- University of Pittsburgh, Department of Epidemiology; Pittsburgh, Pennsylvania, United States of America
| | - Harry Hochheiser
- University of Pittsburgh, Department of Biomedical Informatics, Intelligent Systems Program, and Clinical and Translational Science Institute; Pittsburgh, Pennsylvania, United States of America
| |
Collapse
|
21
|
Reich NG, Wang Y, Burns M, Ergas R, Cramer EY, Ray EL. Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.08.23286582. [PMID: 36945396 PMCID: PMC10029058 DOI: 10.1101/2023.03.08.23286582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.
Collapse
Affiliation(s)
- Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA
| | - Yijin Wang
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA
| | - Meagan Burns
- Massachusetts Department of Public Health, Boston, MA March 8, 2023
| | - Rosa Ergas
- Massachusetts Department of Public Health, Boston, MA March 8, 2023
| | - Estee Y Cramer
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA
| | - Evan L Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA
| |
Collapse
|
22
|
Stenseth NC, Schlatte R, Liu X, Pielke R, Li R, Chen B, Bjørnstad ON, Kusnezov D, Gao GF, Fraser C, Whittington JD, Bai Y, Deng K, Gong P, Guan D, Xiao Y, Xu B, Johnsen EB. How to avoid a local epidemic becoming a global pandemic. Proc Natl Acad Sci U S A 2023; 120:e2220080120. [PMID: 36848570 PMCID: PMC10013804 DOI: 10.1073/pnas.2220080120] [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: 11/28/2022] [Accepted: 01/10/2023] [Indexed: 03/01/2023] Open
Abstract
Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.
Collapse
Affiliation(s)
- Nils Chr. Stenseth
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Rudolf Schlatte
- Department of Informatics, University of Oslo, Oslo0316, Norway
| | - Xiaoli Liu
- Department of Computer Science, University of Helsinki, 00560Helsinki, Finland
| | - Roger Pielke
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Environmental Studies, University of Colorado Boulder, Boulder, CO80309
| | - Ruiyun Li
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Bin Chen
- Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, University of Hong Kong, Hong Kong999077, China
- Department of Geography, Urban Systems Institute, University of Hong Kong, Hong Kong999077, China
- HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong999077, China
| | - Ottar N. Bjørnstad
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA16802
| | - Dimitri Kusnezov
- Deputy Under Secretary, Artificial Intelligence & Technology Office, US Department of Energy, Washington,DC20585
| | - George F. Gao
- Chinese Academy of Sciences Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing100101, China
- Chinese Center for Disease Control and Prevention, Beijing102206, China
| | - Christophe Fraser
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7DQ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford0X3 7LFUK
| | - Jason D. Whittington
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Yuqi Bai
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing100084, China
| | - Peng Gong
- Department of Earth Sciences, University of Hong Kong, Hong Kong999077, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Dabo Guan
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing100193, China
| | - Bing Xu
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
| | | |
Collapse
|
23
|
Pokutnaya D, Childers B, Arcury-Quandt AE, Hochheiser H, Van Panhuis WG. An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases. PLoS Comput Biol 2023; 19:e1010856. [PMID: 36928042 PMCID: PMC10019712 DOI: 10.1371/journal.pcbi.1010856] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.
Collapse
Affiliation(s)
- Darya Pokutnaya
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, Pennsylvania, United States of America
| | - Bruce Childers
- University of Pittsburgh, Department of Computer Science, Pittsburgh, Pennsylvania, United States of America
| | - Alice E. Arcury-Quandt
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, Pennsylvania, United States of America
| | - Harry Hochheiser
- University of Pittsburgh, Department of Biomedical Informatics and Intelligent Systems Program, Pittsburgh, Pennsylvania, United States of America
| | - Willem G. Van Panhuis
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
| |
Collapse
|
24
|
Du H, Dong E, Badr HS, Petrone ME, Grubaugh ND, Gardner LM. Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach. EBioMedicine 2023; 89:104482. [PMID: 36821889 PMCID: PMC9943054 DOI: 10.1016/j.ebiom.2023.104482] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term. METHOD Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases. FINDINGS The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants. INTERPRETATION Based on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk. FUNDING This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
Collapse
Affiliation(s)
- Hongru Du
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Ensheng Dong
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hamada S Badr
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Mary E Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06510, USA
| | - Lauren M Gardner
- Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
| |
Collapse
|
25
|
Mavragani A, Eysenbach G, Ingram D, Khan B, Marsh J, McAndrew T. Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study. JMIR Public Health Surveill 2022; 8:e39336. [PMID: 36219845 PMCID: PMC9822568 DOI: 10.2196/39336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/08/2022] [Accepted: 10/09/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Past research has shown that various signals associated with human behavior (eg, social media engagement) can benefit computational forecasts of COVID-19. One behavior that has been shown to reduce the spread of infectious agents is compliance with nonpharmaceutical interventions (NPIs). However, the extent to which the public adheres to NPIs is difficult to measure and consequently difficult to incorporate into computational forecasts of infectious diseases. Soliciting judgments from many individuals (ie, crowdsourcing) can lead to surprisingly accurate estimates of both current and future targets of interest. Therefore, asking a crowd to estimate community-level compliance with NPIs may prove to be an accurate and predictive signal of an infectious disease such as COVID-19. OBJECTIVE We aimed to show that crowdsourced perceptions of compliance with NPIs can be a fast and reliable signal that can predict the spread of an infectious agent. We showed this by measuring the correlation between crowdsourced perceptions of NPIs and US incident cases of COVID-19 1-4 weeks ahead, and evaluating whether incorporating crowdsourced perceptions improves the predictive performance of a computational forecast of incident cases. METHODS For 36 weeks from September 2020 to April 2021, we asked 2 crowds 21 questions about their perceptions of community adherence to NPIs and public health guidelines, and collected 10,120 responses. Self-reported state residency was compared to estimates from the US census to determine the representativeness of the crowds. Crowdsourced NPI signals were mapped to 21 mean perceived adherence (MEPA) signals and analyzed descriptively to investigate features, such as how MEPA signals changed over time and whether MEPA time series could be clustered into groups based on response patterns. We investigated whether MEPA signals were associated with incident cases of COVID-19 1-4 weeks ahead by (1) estimating correlations between MEPA and incident cases, and (2) including MEPA into computational forecasts. RESULTS The crowds were mostly geographically representative of the US population with slight overrepresentation in the Northeast. MEPA signals tended to converge toward moderate levels of compliance throughout the survey period, and an unsupervised analysis revealed signals clustered into 4 groups roughly based on the type of question being asked. Several MEPA signals linearly correlated with incident cases of COVID-19 1-4 weeks ahead at the US national level. Including questions related to social distancing, testing, and limiting large gatherings increased out-of-sample predictive performance for probabilistic forecasts of incident cases of COVID-19 1-3 weeks ahead when compared to a model that was trained on only past incident cases. CONCLUSIONS Crowdsourced perceptions of nonpharmaceutical adherence may be an important signal to improve forecasts of the trajectory of an infectious agent and increase public health situational awareness.
Collapse
Affiliation(s)
| | | | - David Ingram
- Actuarial Risk Management, Austin, TX, United States
| | - Bilal Khan
- Computer Science and Engineering, Lehigh University, Bethlehem, PA, United States
| | - Jessecae Marsh
- Department of Psychology, Lehigh University, Bethlehem, PA, United States
| | - Thomas McAndrew
- College of Health, Lehigh University, Bethlehem, PA, United States
| |
Collapse
|
26
|
Zavalis EA, Ioannidis JPA. A meta-epidemiological assessment of transparency indicators of infectious disease models. PLoS One 2022; 17:e0275380. [PMID: 36206207 PMCID: PMC9543956 DOI: 10.1371/journal.pone.0275380] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/15/2022] [Indexed: 01/04/2023] Open
Abstract
Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
Collapse
Affiliation(s)
- Emmanuel A. Zavalis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Solna, Stockholm, Sweden
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, California, United States of America
- * E-mail:
| |
Collapse
|
27
|
An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA. PLoS Comput Biol 2022; 18:e1010602. [PMID: 36201534 PMCID: PMC9578588 DOI: 10.1371/journal.pcbi.1010602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 10/18/2022] [Accepted: 09/26/2022] [Indexed: 11/19/2022] Open
Abstract
We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.
Collapse
|
28
|
Nixon K, Jindal S, Parker F, Reich NG, Ghobadi K, Lee EC, Truelove S, Gardner L. An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation. Lancet Digit Health 2022; 4:e738-e747. [PMID: 36150782 PMCID: PMC9489063 DOI: 10.1016/s2589-7500(22)00148-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 06/17/2022] [Accepted: 07/13/2022] [Indexed: 02/06/2023]
Abstract
Infectious disease modelling can serve as a powerful tool for situational awareness and decision support for policy makers. However, COVID-19 modelling efforts faced many challenges, from poor data quality to changing policy and human behaviour. To extract practical insight from the large body of COVID-19 modelling literature available, we provide a narrative review with a systematic approach that quantitatively assessed prospective, data-driven modelling studies of COVID-19 in the USA. We analysed 136 papers, and focused on the aspects of models that are essential for decision makers. We have documented the forecasting window, methodology, prediction target, datasets used, and geographical resolution for each study. We also found that a large fraction of papers did not evaluate performance (25%), express uncertainty (50%), or state limitations (36%). To remedy some of these identified gaps, we recommend the adoption of the EPIFORGE 2020 model reporting guidelines and creating an information-sharing system that is suitable for fast-paced infectious disease outbreak science.
Collapse
Affiliation(s)
- Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sonia Jindal
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Felix Parker
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Kimia Ghobadi
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shaun Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
29
|
Nixon K, Jindal S, Parker F, Marshall M, Reich NG, Ghobadi K, Lee EC, Truelove S, Gardner L. Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation. Lancet Digit Health 2022; 4:e699-e701. [PMID: 36150779 PMCID: PMC9499327 DOI: 10.1016/s2589-7500(22)00167-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/22/2022] [Accepted: 08/11/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sonia Jindal
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Felix Parker
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Kimia Ghobadi
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shaun Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| |
Collapse
|
30
|
Codi A, Luk D, Braun D, Cambeiro J, Besiroglu T, Chen E, de Cesaris LEU, Bocchini P, McAndrew T. Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures. Open Forum Infect Dis 2022; 9:ofac354. [PMID: 35937647 PMCID: PMC9348614 DOI: 10.1093/ofid/ofac354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Indexed: 12/04/2022] Open
Abstract
Aggregated human judgment forecasts for coronavirus disease 2019 (COVID-19) targets of public health importance are accurate, often outperforming computational models. Our work shows that aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as a tool to aid public health decision making during outbreaks.
Collapse
Affiliation(s)
- Allison Codi
- College of Health, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Damon Luk
- College of Health, Lehigh University, Bethlehem, Pennsylvania, USA
| | - David Braun
- Department of Psychology, College of Arts and Sciences, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Juan Cambeiro
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
- Metaculus, Santa Cruz, California, USA
| | - Tamay Besiroglu
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
- Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Eva Chen
- Good Judgment Inc, New York, New York, USA
| | | | - Paolo Bocchini
- Department of Civil and Environmental Engineering, P.C. Rossin College of Engineering and Applied Science, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Thomas McAndrew
- College of Health, Lehigh University, Bethlehem, Pennsylvania, USA
| |
Collapse
|
31
|
Ray EL, Brooks LC, Bien J, Biggerstaff M, Bosse NI, Bracher J, Cramer EY, Funk S, Gerding A, Johansson MA, Rumack A, Wang Y, Zorn M, Tibshirani RJ, Reich NG. Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States. INTERNATIONAL JOURNAL OF FORECASTING 2022:S0169-2070(22)00096-6. [PMID: 35791416 PMCID: PMC9247236 DOI: 10.1016/j.ijforecast.2022.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.
Collapse
Affiliation(s)
- Evan L Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Logan C Brooks
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Jacob Bien
- Department of Data Sciences and Operations, University of Southern California, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America
| | - Nikos I Bosse
- London School of Hygiene & Tropical Medicine, United Kingdom
| | - Johannes Bracher
- Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Germany
| | - Estee Y Cramer
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, United Kingdom
| | - Aaron Gerding
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Michael A Johansson
- COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Yijin Wang
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Martha Zorn
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| | - Ryan J Tibshirani
- Machine Learning Department, Carnegie Mellon University, United States of America
| | - Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America
| |
Collapse
|
32
|
Chowell G, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.. [PMID: 35794886 PMCID: PMC9258290 DOI: 10.1101/2022.06.19.22276608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In the 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework could be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions. The COVID-19 pandemic has highlighted the urgent need to develop reliable tools to forecast the trajectory of epidemics and pandemics in near real-time. We describe and apply an ensemble n-sub-epidemic modeling framework for forecasting the trajectory of epidemics and pandemics. We systematically assess its calibration and short-term forecasting performance in weekly 10–30 days ahead forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022 and compare its performance with two different statistical ARIMA models. This framework demonstrated reliable forecasting performance and substantially outcompeted the ARIMA models. The forecasting performance was consistently best for the ensemble sub-epidemic models incorporating a higher number of top-ranking sub-epidemic models. The ensemble model incorporating the top four ranking sub-epidemic models consistently yielded the best performance, particularly in terms of the coverage rate of the 95% prediction interval and the weighted interval score. This framework can be applied to forecast other growth processes found in nature and society including the spread of information through social media.
Collapse
|
33
|
Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard. Sci Rep 2022; 12:7603. [PMID: 35534601 PMCID: PMC9084272 DOI: 10.1038/s41598-022-11607-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/25/2022] [Indexed: 11/29/2022] Open
Abstract
Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders.
Collapse
|
34
|
Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, Niemi J, Shah A, Stark A, Wang Y, Wattanachit N, Zorn MW, Gu Y, Jain S, Bannur N, Deva A, Kulkarni M, Merugu S, Raval A, Shingi S, Tiwari A, White J, Abernethy NF, Woody S, Dahan M, Fox S, Gaither K, Lachmann M, Meyers LA, Scott JG, Tec M, Srivastava A, George GE, Cegan JC, Dettwiller ID, England WP, Farthing MW, Hunter RH, Lafferty B, Linkov I, Mayo ML, Parno MD, Rowland MA, Trump BD, Zhang-James Y, Chen S, Faraone SV, Hess J, Morley CP, Salekin A, Wang D, Corsetti SM, Baer TM, Eisenberg MC, Falb K, Huang Y, Martin ET, McCauley E, Myers RL, Schwarz T, Sheldon D, Gibson GC, Yu R, Gao L, Ma Y, Wu D, Yan X, Jin X, Wang YX, Chen Y, Guo L, Zhao Y, Gu Q, Chen J, Wang L, Xu P, Zhang W, Zou D, Biegel H, Lega J, McConnell S, Nagraj VP, Guertin SL, Hulme-Lowe C, Turner SD, Shi Y, Ban X, Walraven R, Hong QJ, Kong S, van de Walle A, Turtle JA, Ben-Nun M, Riley S, Riley P, Koyluoglu U, DesRoches D, Forli P, Hamory B, Kyriakides C, Leis H, Milliken J, Moloney M, Morgan J, Nirgudkar N, Ozcan G, Piwonka N, Ravi M, Schrader C, Shakhnovich E, Siegel D, Spatz R, Stiefeling C, Wilkinson B, Wong A, Cavany S, España G, Moore S, Oidtman R, Perkins A, Kraus D, Kraus A, Gao Z, Bian J, Cao W, Ferres JL, Li C, Liu TY, Xie X, Zhang S, Zheng S, Vespignani A, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Xiong X, Zheng A, Baek J, Farias V, Georgescu A, Levi R, Sinha D, Wilde J, Perakis G, Bennouna MA, Nze-Ndong D, Singhvi D, Spantidakis I, Thayaparan L, Tsiourvas A, Sarker A, Jadbabaie A, Shah D, Della Penna N, Celi LA, Sundar S, Wolfinger R, Osthus D, Castro L, Fairchild G, Michaud I, Karlen D, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Lee EC, Dent J, Grantz KH, Hill AL, Kaminsky J, Kaminsky K, Keegan LT, Lauer SA, Lemaitre JC, Lessler J, Meredith HR, Perez-Saez J, Shah S, Smith CP, Truelove SA, Wills J, Marshall M, Gardner L, Nixon K, Burant JC, Wang L, Gao L, Gu Z, Kim M, Li X, Wang G, Wang Y, Yu S, Reiner RC, Barber R, Gakidou E, Hay SI, Lim S, Murray C, Pigott D, Gurung HL, Baccam P, Stage SA, Suchoski BT, Prakash BA, Adhikari B, Cui J, Rodríguez A, Tabassum A, Xie J, Keskinocak P, Asplund J, Baxter A, Oruc BE, Serban N, Arik SO, Dusenberry M, Epshteyn A, Kanal E, Le LT, Li CL, Pfister T, Sava D, Sinha R, Tsai T, Yoder N, Yoon J, Zhang L, Abbott S, Bosse NI, Funk S, Hellewell J, Meakin SR, Sherratt K, Zhou M, Kalantari R, Yamana TK, Pei S, Shaman J, Li ML, Bertsimas D, Lami OS, Soni S, Bouardi HT, Ayer T, Adee M, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller P, Xiao J, Wang Y, Wang Q, Xie S, Zeng D, Green A, Bien J, Brooks L, Hu AJ, Jahja M, McDonald D, Narasimhan B, Politsch C, Rajanala S, Rumack A, Simon N, Tibshirani RJ, Tibshirani R, Ventura V, Wasserman L, O’Dea EB, Drake JM, Pagano R, Tran QT, Ho LST, Huynh H, Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A 2022; 119:e2113561119. [PMID: 35394862 PMCID: PMC9169655 DOI: 10.1073/pnas.2113561119] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 01/15/2023] Open
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
Collapse
Affiliation(s)
- Estee Y. Cramer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Evan L. Ray
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | | | | | - Aaron Gerding
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Tilmann Gneiting
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany
| | - Katie H. House
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yuxin Huang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Dasuni Jayawardena
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Abdul H. Kanji
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ayush Khandelwal
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Khoa Le
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland
| | - Jarad Niemi
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Apurv Shah
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ariane Stark
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yijin Wang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Nutcha Wattanachit
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Martha W. Zorn
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | | | - Sansiddh Jain
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Nayana Bannur
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Ayush Deva
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Mihir Kulkarni
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Srujana Merugu
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Alpan Raval
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Siddhant Shingi
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Avtansh Tiwari
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Jerome White
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | | | - Spencer Woody
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Maytal Dahan
- Texas Advanced Computing Center, Austin, TX 78758
| | - Spencer Fox
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | | | | | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - James G. Scott
- Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712
| | - Mauricio Tec
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712
| | - Ajitesh Srivastava
- Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089
| | - Glover E. George
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Jeffrey C. Cegan
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Ian D. Dettwiller
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | | | | | - Robert H. Hunter
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Brandon Lafferty
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Igor Linkov
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Michael L. Mayo
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Matthew D. Parno
- US Army Engineer Research and Development Center, Hanover, NH 03755
| | | | | | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Samuel Chen
- School of Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Stephen V. Faraone
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Jonathan Hess
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Christopher P. Morley
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Asif Salekin
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13207
| | - Dongliang Wang
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | | | - Thomas M. Baer
- Department of Physics, Trinity University, San Antonio, TX 78212
| | - Marisa C. Eisenberg
- Department of Complex Systems, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Karl Falb
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Yitao Huang
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Emily T. Martin
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Ella McCauley
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Robert L. Myers
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Tom Schwarz
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003
| | - Graham Casey Gibson
- School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115
| | - Liyao Gao
- Department of Statistics, University of Washington, Seattle, WA 98185
| | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
| | - Dongxia Wu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Xifeng Yan
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Xiaoyong Jin
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Yu-Xiang Wang
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - YangQuan Chen
- Mechatronics, Embedded Systems and Automation Lab, Department of Mechanical Engineering, University of California, Merced, CA 95301
| | - Lihong Guo
- Jilin University, Changchun City, Jilin Province, 130012, People's Republic of China
| | - Yanting Zhao
- University of Science and Technology of China, Heifei, Anhui, 230027, People's Republic of China
| | - Quanquan Gu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Jinghui Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Lingxiao Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Pan Xu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Weitong Zhang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Difan Zou
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Hannah Biegel
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | - Joceline Lega
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | | | - V. P. Nagraj
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Stephanie L. Guertin
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | | | - Stephen D. Turner
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Yunfeng Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12309
| | - Xuegang Ban
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | | | - Qi-Jun Hong
- School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287
- School of Engineering, Brown University, Providence, RI 02912
| | | | | | - James A. Turtle
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, W2 1PG London, United Kingdom
| | - Pete Riley
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | | | | | - Pedro Forli
- Oliver Wyman Digital, Oliver Wyman, Sao Paolo, Brazil 04711-904
| | - Bruce Hamory
- Health & Life Sciences, Oliver Wyman, Boston, MA 02110
| | | | - Helen Leis
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - John Milliken
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - James Morgan
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - Gokce Ozcan
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Noah Piwonka
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - Matt Ravi
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Schrader
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | | | - Daniel Siegel
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Ryan Spatz
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Stiefeling
- Financial Services, Oliver Wyman Digital, Toronto, ON, Canada M5J 0A1
| | | | | | - Sean Cavany
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Sean Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Rachel Oidtman
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - David Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | - Andrea Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | | | | | - Wei Cao
- Microsoft, Redmond, WA 98029
| | | | | | | | | | | | | | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, 10133, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Andrew Zheng
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Jackie Baek
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Vivek Farias
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Andreea Georgescu
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deeksha Sinha
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Joshua Wilde
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | | | | | | | - Divya Singhvi
- Technology, Operations and Statistics (TOPS) group, Stern School of Business, New York University, New York, NY 10012
| | | | | | | | - Arnab Sarker
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ali Jadbabaie
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Devavrat Shah
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nicolas Della Penna
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Dave Osthus
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Lauren Castro
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Isaac Michaud
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Dean Karlen
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, Canada
- Physical Sciences Division, TRIUMF, Vancouver, BC, V8W 2Y2, Canada
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Juan Dent
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Kyra H. Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Alison L. Hill
- Institute for Computational Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21218
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | | | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108
| | - Stephen A. Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Hannah R. Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Sam Shah
- Unaffiliated, San Francisco, CA 94122
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Shaun A. Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
- International Vaccine Access Center, Johns Hopkins University, Baltimore, MD 21231
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231
| | | | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Lily Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Lei Gao
- Department of Finance, Iowa State University, Ames, IA 50011
| | - Zhiling Gu
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Xinyi Li
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634
| | - Guannan Wang
- Department of Mathematics, College of William & Mary, Williamsburg, VA 23187
| | - Yueying Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Shan Yu
- Department of Statistics, University of Virginia, Charlottesville, VA 22904
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Steve Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Chris Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | | | | | | | | | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Bijaya Adhikari
- Department of Computer Science, University of Iowa, Iowa City, IA 52242
| | - Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | | | - Anika Tabassum
- Department of Computer Science, Virginia Tech, Falls Church, VA 22043
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - John Asplund
- Advanced Data Analytics, Metron, Inc., Reston, VA 20190
| | - Arden Baxter
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Buse Eylul Oruc
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Nicoleta Serban
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | | | | | | | | | | | | | | | | | | | - Thomas Tsai
- Department of Health Policy and Management, Harvard University, Cambridge, MA 02138
| | | | | | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sophie R. Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Mingyuan Zhou
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712
| | - Rahi Kalantari
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Teresa K. Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Michael L. Li
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Dimitris Bertsimas
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Omar Skali Lami
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Saksham Soni
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Hamza Tazi Bouardi
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
- Winship Cancer Institute, Emory University Medical School, Atlanta, GA 30322
| | - Madeline Adee
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jagpreet Chhatwal
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Ozden O. Dalgic
- Health Economic Modeling, Value Analytics Labs, 34776 İstanbul, Turkey
| | - Mary A. Ladd
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Benjamin P. Linas
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118
| | - Peter Mueller
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jade Xiao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Qinxia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Shanghong Xie
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alden Green
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jacob Bien
- Marshall School of Business, Department of Data Sciences and Operations (DSO), University of Southern California, Los Angeles, CA 90089
| | - Logan Brooks
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J. Hu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Maria Jahja
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Daniel McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Collin Politsch
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Samyak Rajanala
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ryan J. Tibshirani
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Rob Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Valerie Ventura
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | | | - Quoc T. Tran
- Catalog Data Science, Walmart Inc., Sunnyvale, CA 94085
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Huong Huynh
- Virtual Power System Inc, Milpitas, CA 95035
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Nicholas G. Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| |
Collapse
|
35
|
Codi A, Luk D, Braun D, Cambeiro J, Besiroglu T, Chen E, de Cèsaris LEU, Bocchini P, McAndrew T. Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures. ARXIV 2022:2204.02466. [PMID: 35441083 PMCID: PMC9016644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 04/14/2022] [Indexed: 11/23/2022]
Abstract
Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as tool to aid public health decision making during outbreaks.
Collapse
Affiliation(s)
- Allison Codi
- College of Health, Lehigh University, Bethlehem, Pennsylvania, United States of America
| | - Damon Luk
- College of Health, Lehigh University, Bethlehem, Pennsylvania, United States of America
| | - David Braun
- College of Health, Lehigh University, Bethlehem, Pennsylvania, United States of America
| | - Juan Cambeiro
- Metaculus, Santa Cruz, California, United States of America,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States of America
| | - Tamay Besiroglu
- Metaculus, Santa Cruz, California, United States of America,Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Eva Chen
- Good Judgment Inc., New York, New York, United States of America
| | | | - Paolo Bocchini
- Department of Civil and Environmental Engineering, P.C. Rossin College of Engineering and Applied Science, Lehigh University, Bethlehem, Pennsylvania, United States of America
| | - Thomas McAndrew
- College of Health, Lehigh University, Bethlehem, Pennsylvania, United States of America,
| |
Collapse
|