Gabriele Doblhammer , University of Rostock
Daniel Kreft, University of Rostock
Constantin Reinke, University of Rostock
The second wave of SARS-CoV-2 infections began in Germany in October 2020, increased exponentially in November, and remained at high levels well into December, despite various regulatory measures beginning in September 2020 and a lockdown beginning in early November 2020. In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the second wave in Germany. We used COVID-19 diagnoses and deaths from October 1 to December 15, 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. While both social gradients, positive and negative, were present, the negative SES gradient in infections began to dominate over time and was always the dominant one in mortality. Counties with low socioeconomic status had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the “Alternative for Germany” party was among the top characteristics correlated with high incidence and death rates. Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.
Presented in Session P1. Postercafe