000164876 001__ 164876
000164876 005__ 20240308131651.0
000164876 0247_ $$2doi$$a10.1101/2021.04.14.21255474
000164876 0247_ $$2altmetric$$aaltmetric:103894984
000164876 037__ $$aDZNE-2022-01320
000164876 1001_ $$0P:(DE-2719)2811246$$aDoblhammer-Reiter, Gabriele$$b0$$eFirst author
000164876 245__ $$aThe second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach
000164876 260__ $$c2021
000164876 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1709900159_26559
000164876 3367_ $$2ORCID$$aWORKING_PAPER
000164876 3367_ $$028$$2EndNote$$aElectronic Article
000164876 3367_ $$2DRIVER$$apreprint
000164876 3367_ $$2BibTeX$$aARTICLE
000164876 3367_ $$2DataCite$$aOutput Types/Working Paper
000164876 500__ $$amedRxiv preprint
000164876 520__ $$aThere  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.
000164876 536__ $$0G:(DE-HGF)POF4-354$$a354 - Disease Prevention and Healthy Aging (POF4-354)$$cPOF4-354$$fPOF IV$$x0
000164876 588__ $$aDataset connected to CrossRef
000164876 7001_ $$0P:(DE-HGF)0$$aReinke, Constantin$$b1
000164876 7001_ $$0P:(DE-2719)2812837$$aKreft, Daniel$$b2$$eLast author
000164876 773__ $$a10.1101/2021.04.14.21255474$$p1-29
000164876 8564_ $$uhttps://pub.dzne.de/record/164876/files/DZNE-2022-01320_Restricted.pdf
000164876 8564_ $$uhttps://pub.dzne.de/record/164876/files/DZNE-2022-01320_Restricted.pdf?subformat=pdfa$$xpdfa
000164876 909CO $$ooai:pub.dzne.de:164876$$pVDB
000164876 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2811246$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000164876 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812837$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b2$$kDZNE
000164876 9131_ $$0G:(DE-HGF)POF4-354$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vDisease Prevention and Healthy Aging$$x0
000164876 9141_ $$y2021
000164876 9201_ $$0I:(DE-2719)1012002$$kAG Doblhammer$$lDemographic Studies$$x0
000164876 980__ $$apreprint
000164876 980__ $$aVDB
000164876 980__ $$aI:(DE-2719)1012002
000164876 980__ $$aUNRESTRICTED