Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany.

Gabriele Doblhammer,Constantin Reinke,Daniel Kreft

doi:10.3390/ijerph182010663

Abstract

(1) Background: 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 upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 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. (3) Results: Counties with low socioeconomic status (SES) 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 (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Highlights

The second wave of SARS-CoV-2 infections that 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
Taking the first twenty features according to their Shap values (Period 1: Figure S6a,b; Period 2: Figure S7a,b; Period 3: Figure S8a,b; Period 4: Figure S9a,b; Period 5: Figure S10a,b in the Supplementary Materials), we grouped them into the categories outlined above
We counted the number of features in each category and found that features related to socioeconomic status (SES), urbanity/density, and health were present in all time periods; those representing the connectedness of a region were present in the period from mid-October to mid-November and again in December

Summary

Introduction

The second wave of SARS-CoV-2 infections that 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. Information about gender, age, and place of residence is the only available information about SARS-CoV-2 infections and COVID-19 deaths, which has hampered a detailed analysis about the drivers of the second wave. Even during the course of the first wave, there were important factors that were identified both in Germany and internationally.

Results

Discussion

Conclusion