An unbiased spatiotemporal risk model for COVID-19 with epidemiologically meaningful dynamics (A systematic framework for spatiotemporal modelling of COVID-19 disease)

TL;DR

This paper introduces a systematic spatiotemporal framework for modeling COVID-19 risk that accounts for epidemiological metrics and biases, enabling unbiased identification of high-risk periods and regional epidemic dynamics.

Contribution

The authors develop a refined, unbiased risk model that corrects for population and testing heterogeneity, advancing the accuracy of COVID-19 spatiotemporal analysis.

Findings

Unbiased identification of high-risk periods in COVID-19

Insights into regional testing prioritization

Analysis of epidemic synchronization effects

Abstract

Spatiotemporal modelling of infectious diseases such as COVID-19 involves using a variety of epidemiological metrics such as regional proportion of cases or regional positivity rates. Although observing their changes over time is critical to estimate the regional disease burden, the dynamical properties of these measures as well as cross-relationships are not systematically explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk model that is biased neither by the differences in population sizes nor by the spatial heterogeneity of testing. In particular, the proposed methodology is useful for the unbiased identification of time periods with elevated COVID-19 risk, without sensitivity to spatial heterogeneity of neither population nor testing. Our results also provide insights regarding regional prioritization of testing and the consequences of potential synchronization of epidemics between regions.