Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany

•At-risk districts for SARS-CoV-2 incidence can be detected with high certainty.•The Baysian approach complements the spatial monitoring of the SARS-CoV-2 pandemic.•Spatial and temporal dynamics of disease incidence are covered by the model.•Provides an informative and timely basis for local policy planning.•The approach may serve as blueprint for monitoring approaches elsewhere. Timely monitoring of incidence risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated deaths at small-area level is essential to inform containment strategies. We analysed the spatiotemporal epidemiology of the SARSCoV- 2 pandemic at district level in Germany to develop a tool for disease monitoring. We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior probability (PP) for exceedance of RR thresholds 1, 2 or 3. Of 220 districts (55% of 401 districts) showing a RR > 1, 188 (47%) exceed the RR threshold with sufficient certainty (PP ≥ 80%) to be considered at high risk. 47 districts show very high (RR > 2, PP ≥ 80%) and 15 extremely high (RR > 3, PP ≥ 80%) risks. The spatial approach for monitoring the risk of SARS-CoV-2 provides an informative basis for local policy planning.