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

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.