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@ARTICLE{DoblhammerReiter:162833,
      author       = {Doblhammer-Reiter, Gabriele and Kreft, Daniel and Reinke,
                      Constantin},
      title        = {{R}egional {C}haracteristics of the {S}econd {W}ave of
                      {SARS}-{C}o{V}-2 {I}nfections and {COVID}-19 {D}eaths in
                      {G}ermany.},
      journal      = {International journal of environmental research and public
                      health},
      volume       = {18},
      number       = {20},
      issn         = {1660-4601},
      address      = {Basel},
      publisher    = {MDPI AG},
      reportid     = {DZNE-2021-01488},
      pages        = {10663},
      year         = {2021},
      note         = {(CC BY)},
      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.},
      keywords     = {COVID-19 / Germany: epidemiology / Humans / Incidence /
                      Income / SARS-CoV-2 / Shap values (Other) / boosting models
                      (Other) / incidence (Other) / machine learning (Other) /
                      mortality (Other)},
      cin          = {AG Doblhammer-Reiter},
      ddc          = {610},
      cid          = {I:(DE-2719)1012002},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34682408},
      pmc          = {pmc:PMC8535595},
      doi          = {10.3390/ijerph182010663},
      url          = {https://pub.dzne.de/record/162833},
}