Estimating geographic variation of infection fatality ratios during epidemics  

作　　者：Joshua Ladau Eoin L.Brodie Nicola Falco Ishan Bansal Elijah B.Hoffman Marcin P.Joachimiak Ana M.Mora Angelica M.Walker Haruko M.Wainwright Yulun Wu Mirko Pavicic Daniel Jacobson Matthias Hess James B.Brown Katrina Abuabara 

机构地区：[1]Departments of Computational Precision Health and Dermatology,University of California,San Francisco,CA,94115,USA [2]Arva Intelligence,Inc.,Salt Lake City,UT,84101,USA [3]Computational Biosciences Group,Lawrence Berkeley National Laboratory,Berkeley,CA,94720,USA [4]Earth and Environmental Sciences Area,Lawrence Berkeley National Laboratory,Berkeley,CA,94720,USA [5]Graduate Group in Biostatistics,University of California,Berkeley,CA,94720,USA [6]Biosystems Data Science,Environmental Genomics and Systems Biology,Lawrence Berkeley National Laboratory,Berkeley,CA,94720,USA [7]Center for Environmental Research and Community Health(CERCH),School of Public Health,University of California,Berkeley,CA,94720,USA [8]Bredesen Center for Interdisciplinary Research and Graduate Education,University of Tennessee,Knoxville,TN,37996,USA [9]Department of Nuclear Science and Engineering,Massachusetts Institute of Technology,Boston,MA,02139,USA [10]Biosciences,Oak Ridge National Laboratory,Oak Ridge,TN,37830,USA [11]University of California,Davis,CA,95616,USA [12]Statistics Department,University of California,Berkeley,CA,94720,USA [13]Division of Epidemiology and Biostatistics,University of California Berkeley School of Public Health,2121 Berkeley Way,Berkeley,CA,94720,USA

出　　处：《Infectious Disease Modelling》2024年第2期634-643,共10页传染病建模（英文）

基　　金：K.A.and J.L.were supported by a grant from the Benioff Center for Microbiome Medicine;This research used resources of the Oak Ridge Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725;This manuscript has been coauthored by UT-Battelle,LLC under contract no.DE-AC05-00OR22725 with the U.S.Department of Energy;The United States Government retains and the publisher,by accepting the article for publication,acknowledges that the United States Government retains a nonexclusive,paid-up,irrevocable,world-wide license to publish or reproduce the published form of this manuscript,or allow others to do so,for United States Government purposes;The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan,last accessed September 16,2020);Work at Oak Ridge and Lawrence Berkeley National Laboratories was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory,a consortium of DOE national laboratories focused on response to COVID-19,with funding provided by the Coronavirus CARES Act;was facilitated by previous breakthroughs obtained through the Laboratory Directed Research and Development Programs of the Lawrence Berkeley and Oak Ridge National Laboratories.M.P.J.was supported by a grant from the Laboratory Directed Research and Development(LDRD)Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231;Oak Ridge National Laboratory would also like to acknowledge funding from the U.S.National Science Foundation(EF-2133763).

摘　　要：Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

关 键 词：Infection fatality ratio Infection fatality rate Noncentral hypergeometric distribution COVID-19 SARS-CoV-2 

分 类 号：R181.3[医药卫生—流行病学]