Estimating geographic variation of infection fatality ratios during epidemics.

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

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

Afiliación

Ladau J; Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA.
Brodie EL; Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA.
Falco N; Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Bansal I; Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Hoffman EB; Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Joachimiak MP; Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Mora AM; Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA.
Walker AM; Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA.
Wainwright HM; Biosystems Data Science, Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Wu Y; Center for Environmental Research and Community Health (CERCH), School of Public Health, University of California, Berkeley, CA, 94720, USA.
Pavicic M; Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, 37996, USA.
Jacobson D; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Boston, MA, 02139, USA.
Hess M; Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA.
Brown JB; Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Abuabara K; Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.

Infect Dis Model ; 9(2): 634-643, 2024 Jun.

Article en En | MEDLINE | ID: mdl-38572058

ABSTRACT

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.