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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.
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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
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Das P, Igoe M, Lacy A, Farthing T, Timsina A, Lanzas C, Lenhart S, Odoi A, Lloyd AL. Modeling county level COVID-19 transmission in the greater St. Louis area: Challenges of uncertainty and identifiability when fitting mechanistic models to time-varying processes. Math Biosci 2024; 371:109181. [PMID: 38537734 DOI: 10.1016/j.mbs.2024.109181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
We use a compartmental model with a time-varying transmission parameter to describe county level COVID-19 transmission in the greater St. Louis area of Missouri and investigate the challenges in fitting such a model to time-varying processes. We fit this model to synthetic and real confirmed case and hospital discharge data from May to December 2020 and calculate uncertainties in the resulting parameter estimates. We also explore non-identifiability within the estimated parameter set. We find that the death rate of infectious non-hospitalized individuals, the testing parameter and the initial number of exposed individuals are not identifiable based on an investigation of correlation coefficients between pairs of parameter estimates. We also explore how this non-identifiability ties back into uncertainties in the estimated parameters and find that it inflates uncertainty in the estimates of our time-varying transmission parameter. However, we do find that R0 is not highly affected by non-identifiability of its constituent components and the uncertainties associated with the quantity are smaller than those of the estimated parameters. Parameter values estimated from data will always be associated with some uncertainty and our work highlights the importance of conducting these analyses when fitting such models to real data. Exploring identifiability and uncertainty is crucial in revealing how much we can trust the parameter estimates.
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Affiliation(s)
- Praachi Das
- Biomathematics Graduate Program, Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Morganne Igoe
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Alexanderia Lacy
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Trevor Farthing
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Archana Timsina
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Cristina Lanzas
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostics Sciences, University of Tennessee, Knoxville, TN, USA
| | - Alun L Lloyd
- Biomathematics Graduate Program, Department of Mathematics, North Carolina State University, Raleigh, NC, USA.
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Awasthi A, Minin VM, Huang J, Chow D, Xu J. Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic. Stat Med 2023; 42:5189-5206. [PMID: 37705508 DOI: 10.1002/sim.9907] [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: 03/02/2022] [Revised: 07/28/2023] [Accepted: 09/01/2023] [Indexed: 09/15/2023]
Abstract
Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.
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Affiliation(s)
- Achal Awasthi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Volodymyr M Minin
- Department of Statistics, University of California, Irvine, Irvine, California, USA
| | - Jenny Huang
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
| | - Daniel Chow
- School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Jason Xu
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
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Hill DT, Alazawi MA, Moran EJ, Bennett LJ, Bradley I, Collins MB, Gobler CJ, Green H, Insaf TZ, Kmush B, Neigel D, Raymond S, Wang M, Ye Y, Larsen DA. Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study. Infect Dis Model 2023; 8:1138-1150. [PMID: 38023490 PMCID: PMC10665827 DOI: 10.1016/j.idm.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Background The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. Methods Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. Findings Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]). Interpretation Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.
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Affiliation(s)
- Dustin T. Hill
- Department of Public Health, Syracuse University, Syracuse, NY, 13244, USA
| | - Mohammed A. Alazawi
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
| | - E. Joe Moran
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
- CDC Foundation, Atlanta, GA, USA
| | - Lydia J. Bennett
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
- CDC Foundation, Atlanta, GA, USA
| | - Ian Bradley
- Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
| | - Mary B. Collins
- School of Marine and Atmospheric Sciences, Sustainability Studies Division, Stony Brook University, Stony Brook, NY, USA
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA
| | - Christopher J. Gobler
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, USA
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Hyatt Green
- Department of Environmental Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA
| | - Tabassum Z. Insaf
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
- Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, Rensselaer, NY, USA
| | - Brittany Kmush
- Department of Public Health, Syracuse University, Syracuse, NY, 13244, USA
| | - Dana Neigel
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
- CDC Foundation, Atlanta, GA, USA
| | - Shailla Raymond
- Center for Environmental Health, New York State Department of Health, Albany, NY, USA
- CDC Foundation, Atlanta, GA, USA
| | - Mian Wang
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, USA
- Department of Civil Engineering, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Yinyin Ye
- Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
| | - David A. Larsen
- Department of Public Health, Syracuse University, Syracuse, NY, 13244, USA
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Bayer D, Goldstein IH, Fintzi J, Lumbard K, Ricotta E, Warner S, Busch LM, Strich JR, Chertow DS, Parker DM, Boden-Albala B, Dratch A, Chhuon R, Quick N, Zahn M, Minin VM. Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data. ARXIV 2023:arXiv:2009.02654v3. [PMID: 32908946 PMCID: PMC7480029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Mechanistic models fit to streaming surveillance data are critical to understanding the transmission dynamics of an outbreak as it unfolds in real-time. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We model the transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparametrically. We compare our base model against modified versions which do not use diagnostics test counts or seroprevalence data to demonstrate the utility of including these often unused data streams. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March 2020 and February 2021 and find that 32-72% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest that the abrupt end of the winter surge in January 2021 was due to both behavioral changes and a high level of accumulated natural immunity.
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Affiliation(s)
- Damon Bayer
- Department of Statistics, University of California, Irvine, California, U.S.A
| | - Isaac H. Goldstein
- Department of Statistics, University of California, Irvine, California, U.S.A
| | - Jonathan Fintzi
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, Maryland, U.S.A
| | - Keith Lumbard
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, Maryland, U.S.A
| | - Emily Ricotta
- Epidemiology Unit, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, U.S.A
| | - Sarah Warner
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Lindsay M. Busch
- Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Jeffrey R. Strich
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Daniel S. Chertow
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Daniel M. Parker
- Susan and Henry Samueli College of Health Sciences, University of California, Irvine, California, U.S.A
| | - Bernadette Boden-Albala
- Susan and Henry Samueli College of Health Sciences, University of California, Irvine, California, U.S.A
| | - Alissa Dratch
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | - Richard Chhuon
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | | | - Matthew Zahn
- Orange County Health Care Agency, Santa Ana, California, U.S.A
| | - Volodymyr M. Minin
- Department of Statistics, University of California, Irvine, California, U.S.A
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6
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Marcassoli A, Leonardi M, Passavanti M, De Angelis V, Bentivegna E, Martelletti P, Raggi A. Lessons Learned from the Lessons Learned in Public Health during the First Years of COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1785. [PMID: 36767152 PMCID: PMC9914715 DOI: 10.3390/ijerph20031785] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
(1) Objectives: to investigate the main lessons learned from the public health (PH) response to COVID-19, using the global perspective endorsed by the WHO pillars, and understand what countries have learned from their practical actions. (2) Methods: we searched for articles in PubMed and CINAHL from 1 January 2020 to 31 January 2022. 455 articles were included. Inclusion criteria were PH themes and lessons learned from the COVID-19 pandemic. One hundred and forty-four articles were finally included in a detailed scoping review. (3) Findings: 78 lessons learned were available, cited 928 times in the 144 articles. Our review highlighted 5 main lessons learned among the WHO regions: need for continuous coordination between PH institutions and organisations (1); importance of assessment and evaluation of risk factors for the diffusion of COVID-19, identifying vulnerable populations (2); establishment of evaluation systems to assess the impact of planned PH measures (3); extensive application of digital technologies, telecommunications and electronic health records (4); need for periodic scientific reviews to provide regular updates on the most effective PH management strategies (5). (4) Conclusion: lessons found in this review could be essential for the future, providing recommendations for an increasingly flexible, fast and efficient PH response to a healthcare emergency such as the COVID-19 pandemic.
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Affiliation(s)
- Alessia Marcassoli
- Neurology, Public Health, Disability Unit and Coma Research Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit and Coma Research Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Marco Passavanti
- Neurology, Public Health, Disability Unit and Coma Research Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Valerio De Angelis
- Department of Clinical and Molecular Medicine, Sapienza University, 00189 Rome, Italy
| | - Enrico Bentivegna
- Department of Clinical and Molecular Medicine, Sapienza University, 00189 Rome, Italy
| | - Paolo Martelletti
- Department of Clinical and Molecular Medicine, Sapienza University, 00189 Rome, Italy
| | - Alberto Raggi
- Neurology, Public Health, Disability Unit and Coma Research Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
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7
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Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
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Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
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8
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Nelson K, Lopman B. The hiatus of the handshake. Science 2022; 377:33-34. [PMID: 35771922 DOI: 10.1126/science.abp9316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Human contact has been altered in ways that may affect endemic infections for years to come.
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Affiliation(s)
- Kristin Nelson
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ben Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.,Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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9
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Tran TNA, Wikle NB, Yang F, Inam H, Leighow S, Gentilesco B, Chan P, Albert E, Strong ER, Pritchard JR, Hanage WP, Hanks EM, Crawford FW, Boni MF. SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021. JAMA Netw Open 2022; 5:e2214171. [PMID: 35616938 PMCID: PMC9136627 DOI: 10.1001/jamanetworkopen.2022.14171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated. OBJECTIVE To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. DESIGN, SETTING, AND PARTICIPANTS This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021. EXPOSURES COVID-19-positive test result reported to state department of health. MAIN OUTCOMES AND MEASURES The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated. RESULTS At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive. CONCLUSIONS AND RELEVANCE These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.
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Affiliation(s)
- Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
| | - Nathan B. Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | - Scott Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | | | - Philip Chan
- Department of Medicine, Brown University, Providence, Rhode Island
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park
| | - Emily R. Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Justin R. Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | - William P. Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Ephraim M. Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Department of Statistics and Data Science, Yale University, New Haven, Connecticut
| | - Maciej F. Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
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10
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Kaplan EH, Zulli A, Sanchez M, Peccia J. Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection. Sci Rep 2022; 12:3487. [PMID: 35241744 PMCID: PMC8894397 DOI: 10.1038/s41598-022-07523-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 02/21/2022] [Indexed: 11/29/2022] Open
Abstract
Monitoring the progression of SARS-CoV-2 outbreaks requires accurate estimation of the unobservable fraction of the population infected over time in addition to the observed numbers of COVID-19 cases, as the latter present a distorted view of the pandemic due to changes in test frequency and coverage over time. The objective of this report is to describe and illustrate an approach that produces representative estimates of the unobservable cumulative incidence of infection by scaling the daily concentrations of SARS-CoV-2 RNA in wastewater from the consistent population contribution of fecal material to the sewage collection system.
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Affiliation(s)
- Edward H Kaplan
- Yale School of Management, Yale University, New Haven, CT, 06520, USA.
- Yale School of Public Health, Yale University, New Haven, CT, 06520, USA.
- Department of Chemical and Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, 06520, USA.
| | - Alessandro Zulli
- Department of Chemical and Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, 06520, USA
| | - Marcela Sanchez
- Department of Chemical and Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, 06520, USA
| | - Jordan Peccia
- Department of Chemical and Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, 06520, USA
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11
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Guan J, Zhao Y, Wei Y, Shen S, You D, Zhang R, Lange T, Chen F. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:89-109. [PMID: 35658113 PMCID: PMC9047651 DOI: 10.1515/mr-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/03/2022] [Indexed: 12/20/2022]
Abstract
Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.
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Affiliation(s)
- Jinxing Guan
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yang Zhao
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China.,Center of Biomedical BigData, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongyue Wei
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sipeng Shen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dongfang You
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruyang Zhang
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Theis Lange
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Feng Chen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
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12
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Crawford FW, Jones SA, Cartter M, Dean SG, Warren JL, Li ZR, Barbieri J, Campbell J, Kenney P, Valleau T, Morozova O. Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data. SCIENCE ADVANCES 2022; 8:eabi5499. [PMID: 34995121 PMCID: PMC8741180 DOI: 10.1126/sciadv.abi5499] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/17/2021] [Indexed: 05/06/2023]
Abstract
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.
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Affiliation(s)
- Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Yale School of Management, New Haven, CT, USA
| | - Sydney A. Jones
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Infectious Diseases Section, Connecticut Department of Public Health, Hartford, CT, USA
| | - Matthew Cartter
- Infectious Diseases Section, Connecticut Department of Public Health, Hartford, CT, USA
| | - Samantha G. Dean
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Zehang Richard Li
- Department of Statistics, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | | | | | | | - Olga Morozova
- Program in Public Health and Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
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13
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Tran TNA, Wikle NB, Yang F, Inam H, Leighow S, Gentilesco B, Chan P, Albert E, Strong ER, Pritchard JR, Hanage WP, Hanks EM, Crawford FW, Boni MF. SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.12.06.21267375. [PMID: 34909789 PMCID: PMC8669856 DOI: 10.1101/2021.12.06.21267375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Estimating an infectious disease attack rate requires inference on the number of reported symptomatic cases of a disease, the number of unreported symptomatic cases, and the number of asymptomatic infections. Population-level immunity can then be estimated as the attack rate plus the number of vaccine recipients who had not been previously infected; this requires an estimate of the fraction of vaccines that were distributed to seropositive individuals. To estimate attack rates and population immunity in southern New England, we fit a validated dynamic epidemiological model to case, clinical, and death data streams reported by Rhode Island, Massachusetts, and Connecticut for the first 15 months of the COVID-19 pandemic, from March 1 2020 to May 31 2021. This period includes the initial spring 2020 wave, the major winter wave of 2020-2021, and the lagging wave of lineage B.1.1.7(Alpha) infections during March-April 2021. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in southern New England was still below 15%, setting the stage for a large winter wave. After the roll-out of vaccines in early 2021, population immunity in many states was expected to approach 70% by spring 2021, with more than half of this immune population coming from vaccinations. Our population immunity estimates for May 31 2021 are 73.4% (95% CrI: 72.9% - 74.1%) for Rhode Island, 64.1% (95% CrI: 64.0% - 64.4%) for Connecticut, and 66.3% (95% CrI: 65.9% - 66.9%) for Massachusetts, indicating that >33% of southern Englanders were still susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned due to 34% (Rhode Island), 25% (Connecticut), and 28% (Massachusetts) of vaccine distribution going to seropositive individuals. Future emergency-setting vaccination planning will likely have to consider over-vaccination as a strategy to ensure that high levels of population immunity are reached during the course of an ongoing epidemic.
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Affiliation(s)
- Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | - Nathan B Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - Scott Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | | | - Philip Chan
- Department of Medicine, Brown University, Providence, RI
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park, PA
| | - Emily R Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Justin R Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Ephraim M Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Forrest W Crawford
- Department of Biostatistics, Yale Schools of Public Health, Yale University, New Haven, CT
- Department of Biostatistics, Yale Schools of Public Health, Yale University, New Haven, CT
| | - Maciej F Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
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