AUTHOR=Lawson Andrew B. , Xin Yao 

TITLE=COVID-19 latent age-specific mortality in US states: a county-level spatio-temporal analysis with counterfactuals

JOURNAL=Frontiers in Epidemiology

VOLUME=Volume 4 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/epidemiology/articles/10.3389/fepid.2024.1403212

DOI=10.3389/fepid.2024.1403212

ISSN=2674-1199

ABSTRACT=During the Covid-19 pandemic that occupied much of 2020-2023 and beyond, daily case and death counts were recorded globally. In this study we examine available mortality counts and associated case counts, with a focus on the estimation of missing information concerning age distributions. In this paper we explore a model-based paradigm for generating age distributions of mortality counts in a spatio-temporal context. We pursue this aim by employing Bayesian spatio-temporal lagged dependence models for weekly mortality at the county level. We compare 3 US states at county level: South Carolina, Ohio, New Jersey.Models were developed for mortality counts using Bayesian spatio-temporal constructs with both dependence on current and cumulative case counts and with lagged dependence on previous deaths. Age dependence was predicted from total deaths in proportion to population estimates. This latent age field is generated as counterfactuals and then compared to observed deaths within age groups.The optimal retrospective space-time models for weekly mortality counts were found to be those with lagged dependence and a function of case load. Added random effects were found to vary and while Ohio favored a spatial correlated model, SC, and NJ were found to favor a simpler formulation. The generation of age -specific latent fields was performed for SC only and compared to a 15 month and 13 county data set of observed >65 age population.