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

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