OBJECTIVES : During fall and winter 2020/2021, before vaccine availability, Germany experienced a severe second wave of the COVID‑19 pandemic. Daily cases grew exponentially in October/November (5Oct to 5Nov 2020, phase I), plateaued in November (6Nov to 6Dec 2020, phase II), peaked in late December, and declined in January/February (12Jan to 12Feb 2021, phase III).

We investigated whether socio-economic characteristics (population density (inhabitants/km²; 2019; “popDens”), household size (average number of persons/household; 2011; “hhSize”), average living space (m²/inhabitant; 2011; “livSpace”), education level (percentage of inhabitants with university-entrance qualification; 2019; “Abitur”) and disposable income (EUR/inhabitant; 2018; “income”)) predicted regional differences in incidence (cases per 100,000 inhabitants; “cases/100k”) in each of the three phases. We expected counties with greater living space, education level and disposable income to report lower incidence.

METHODS : County-level daily COVID-19 cases were extracted from RKI databases. County-level predictor variables were retrieved from public sources. For each phase, we computed a robust linear regression model with popDens, hhSize, livSpace, Abitur, and income as predictor variables for county-level cases/100k. Analyses were performed using statistical software R.

RESULTS : For phase I, cases/100k significantly increased with popDens (beta=0.17, p<0.001), hhSize (beta=0.41, p<0.001), and income (beta=0.20, p<0.001), and decreased with livSpace (beta=-0.22, p<0.001), R²=0.32. For phase II, cases/100k significantly increased with popDens (beta=0.12, p<0.001), hhSize (beta=0.39, p<0.001), and income (beta=0.15, p<0.001) and decreased with Abitur (beta=-0.15, p=0.002) and livSpace (beta=-0.24, p<0.001), R²=0.26. For phase III, cases/100k were significantly decreasing with popDens (beta=-0.06, p<0.001), hhSize (beta=-0.14, p=0.031), income (beta=-0.16, p<0.001), and livSpace (beta=-0.21, p<0.001), R²=0.08.

CONCLUSIONS : Socio-economic regional differences influenced COVID‑19 incidence in Germany´s second wave, however the explanatory power was low. The inclusion of influence factors was limited by data availability. The relevant factors differed between phase I/II and phase III. Our research does not claim a causative relationship between variables.