  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.
