Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.
Spat Stat
; 50: 100584, 2022 Aug.
Article
in English
| MEDLINE | ID: covidwho-1612025
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Spat Stat
Year:
2022
Document Type:
Article
Affiliation country:
J.spasta.2021.100584
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