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

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

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8730676 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature