Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Abstract

Abstract The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35–40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available.