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

doi:10.1016/j.idm.2024.02.009

Estimating geographic variation of infection fatality ratios during epidemics

Infect Dis Model. 2024 Mar 4;9(2):634-643. doi: 10.1016/j.idm.2024.02.009. eCollection 2024 Jun.

Authors

Joshua Ladau^{1

2

3}, Eoin L Brodie⁴, Nicola Falco⁴, Ishan Bansal³, Elijah B Hoffman^{2

5}, Marcin P Joachimiak⁶, Ana M Mora⁷, Angelica M Walker⁸, Haruko M Wainwright⁹, Yulun Wu⁵, Mirko Pavicic¹⁰, Daniel Jacobson¹⁰, Matthias Hess¹¹, James B Brown^{2

3

12}, Katrina Abuabara^{1

13}

Affiliations

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.

Abstract

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.

Keywords: COVID-19; Infection fatality rate; Infection fatality ratio; Noncentral hypergeometric distribution; SARS-CoV-2.