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@ARTICLE{DoblhammerReiter:164876,
      author       = {Doblhammer-Reiter, Gabriele and Reinke, Constantin and
                      Kreft, Daniel},
      title        = {{T}he second wave of {SARS}-{C}o{V}-2 infections and
                      {COVID}-19 deaths in {G}ermany – driven by values, social
                      status and migration background? {A} county-scale
                      explainable machine learning approach},
      reportid     = {DZNE-2022-01320},
      pages        = {1-29},
      year         = {2021},
      note         = {medRxiv preprint},
      abstract     = {There is a general consensus that SARS-CoV-2 infections and
                      COVID-19 deaths have hit lower social groups the hardest,
                      however, for Germany individual level information on
                      socioeconomic characteristics of infections and deaths does
                      not exist. The aim of this study was to identify the key
                      features explaining SARS-CoV-2 infections and COVID-19
                      deaths during the upswing of the second wave in Germany. We
                      considered information on COVID-19 diagnoses and deaths from
                      1. October to 15. December 2020 on the county-level,
                      differentiating five two-week time periods. We used 155
                      indicators to characterize counties in nine geographic,
                      social, demographic, and health domains. For each period, we
                      calculated directly age-standardized COVID-19 incidence and
                      death rates on the county level. We trained gradient
                      boosting models to predict the incidence and death rates
                      with the 155 characteristics of the counties for each
                      period. To explore the importance and the direction of the
                      correlation of the regional indicators we used the SHAP
                      procedure. We categorized the top 20 associations identified
                      by the Shapley values into twelve categories depicting the
                      correlation between the feature and the outcome. We found
                      that counties with low SES were important drivers in the
                      second wave, as were those with high international migration
                      and a high proportion of foreigners and a large nursing home
                      population. 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. We concluded
                      that risky working conditions with reduced opportunities for
                      social distancing and a high chronic disease burden put
                      populations in low -SES counties at higher risk of
                      SARS-CoV-2 infections and COVID-19 deaths. In addition,
                      noncompliance with Corona measures and spill-over effects
                      from neighbouring counties increased the spread of the
                      virus. To further substantiate this finding, we urgently
                      need more data at the individual level.},
      cin          = {AG Doblhammer},
      cid          = {I:(DE-2719)1012002},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.04.14.21255474},
      url          = {https://pub.dzne.de/record/164876},
}