Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States

• Type of Material: Article
• Authors: Yuzi Zhang, Emory University; Howard Chang, Emory University; Danielle Iuliano, Centers for Disease Control and Prevention, Atlanta; Carrie Reed, Emory University
• Language: English
• Date: 2022-08-01
• Publisher: ELSEVIER SCI LTD
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Elsevier B.V. All rights reserved.
• Final Published Version (URL): http://dx.doi.org/10.1016/j.spasta.2021.100584
• Title of Journal or Parent Work: SPATIAL STATISTICS
• Volume: 50
• Start Page: 100584
• End Page: 100584
• Grant/Funding Information: This work was supported in part by the National Institutes of Health, USA Award R01AI125842. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funder.
• Keywords: Remote Sensing; EXCESS MORTALITY; Mathematics, Interdisciplinary Applications; INFLUENZA; Physical Sciences; Statistics & Probability; COVID-19; Mathematics; Spatial-temporal modeling; Excess mortality; Bayesian hierarchical modeling; Geology; Science & Technology; Technology; Geosciences, Multidisciplinary
• Research Categories: Biology, Biostatistics; Health Sciences, Public Health

Abstract

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged ≥85 years who died. First, we used a spatial–temporal binomial regression model to estimate the percent of positive SARS-CoV-2 test results. A spatial–temporal negative-binomial model was then used to estimate unrecognized COVID-19 deaths by exploiting the spatial–temporal association between SARS-CoV-2 percent positive and all-cause mortality counts using an excess mortality approach. Computationally efficient Bayesian inference was accomplished via the Polya-Gamma representation of the binomial and negative-binomial models. Among those aged ≥85 years, we estimated 58,200 (95% CI: 51,300, 64,900) unrecognized COVID-19 deaths, which accounts for 26% (95% CI: 24%, 29%) of total COVID-19 deaths in this age group. Our modeling results suggest that COVID-19 mortality and the proportion of unrecognized deaths among deaths attributed to COVID-19 vary by time and across states.