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Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.
Ledebur, Katharina; Kaleta, Michaela; Chen, Jiaying; Lindner, Simon D; Matzhold, Caspar; Weidle, Florian; Wittmann, Christoph; Habimana, Katharina; Kerschbaumer, Linda; Stumpfl, Sophie; Heiler, Georg; Bicher, Martin; Popper, Nikolas; Bachner, Florian; Klimek, Peter.
  • Ledebur K; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
  • Kaleta M; Complexity Science Hub Vienna, Vienna, Austria.
  • Chen J; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
  • Lindner SD; Complexity Science Hub Vienna, Vienna, Austria.
  • Matzhold C; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
  • Weidle F; Complexity Science Hub Vienna, Vienna, Austria.
  • Wittmann C; Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Habimana K; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
  • Kerschbaumer L; Complexity Science Hub Vienna, Vienna, Austria.
  • Stumpfl S; Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
  • Heiler G; Complexity Science Hub Vienna, Vienna, Austria.
  • Bicher M; Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria.
  • Popper N; Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria.
  • Bachner F; Austrian National Public Health Institute, Vienna, Austria.
  • Klimek P; Austrian National Public Health Institute, Vienna, Austria.
PLoS Comput Biol ; 18(4): e1009973, 2022 04.
Article in English | MEDLINE | ID: covidwho-2021460
ABSTRACT
The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1009973

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1009973