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ORIGINAL RESEARCH article

Front. Epidemiol.
Sec. Infectious Disease Epidemiology
Volume 4 - 2024 | doi: 10.3389/fepid.2024.1403212
This article is part of the Research Topic Insights in Infectious Disease Epidemiology: 2023 View all 4 articles

Covid-19 Latent Age-Specific Mortality in US states: a county level spatio-temporal analysis with counterfactuals

Provisionally accepted
Andrew Lawson Andrew Lawson 1,2*Yao Xin Yao Xin 1
  • 1 Medical University of South Carolina, Charleston, United States
  • 2 Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, Scotland, United Kingdom

The final, formatted version of the article will be published soon.

    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.

    Keywords: Bayesian, counterfactuals, Spatio-temporal, COVID-19, Mortality, age-specific

    Received: 19 Mar 2024; Accepted: 15 Oct 2024.

    Copyright: © 2024 Lawson and Xin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Andrew Lawson, Medical University of South Carolina, Charleston, United States

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