question: 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.