Estimating geographic variation of infection fatality ratios during epidemics

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

Infectious Disease Modelling (Jun 2024)

Estimating geographic variation of infection fatality ratios during epidemics

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

Affiliations

Joshua Ladau: Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA; Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA; Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Corresponding author. Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA.
Eoin L. Brodie: Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Nicola Falco: Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Ishan Bansal: Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Elijah B. Hoffman: Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA; Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA
Marcin P. Joachimiak: Biosystems Data Science, Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Ana M. Mora: Center for Environmental Research and Community Health (CERCH), School of Public Health, University of California, Berkeley, CA, 94720, USA
Angelica M. Walker: Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, 37996, USA
Haruko M. Wainwright: Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Boston, MA, 02139, USA
Yulun Wu: Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA
Mirko Pavicic: Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
Daniel Jacobson: Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
Matthias Hess: University of California, Davis, CA, 95616, USA
James B. Brown: Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA; Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Statistics Department, University of California, Berkeley, CA, 94720, USA
Katrina Abuabara: Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA; Division of Epidemiology and Biostatistics, University of California Berkeley School of Public Health, 2121 Berkeley Way, Berkeley, CA, 94720, USA

Journal volume & issue: Vol. 9, no. 2
pp. 634 – 643

Abstract

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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.

Published in Infectious Disease Modelling

ISSN: 2468-0427 (Online)
Publisher: KeAi Communications Co., Ltd.
Country of publisher: China
LCC subjects: Medicine: Internal medicine: Infectious and parasitic diseases
Website: https://www.keaipublishing.com/en/journals/infectious-disease-modelling/

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