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气象因素和非药物干预措施解释了 SARS-CoV-2 在奥地利传播的局部差异。

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

机构信息

Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.

Complexity Science Hub Vienna, Vienna, Austria.

出版信息

PLoS Comput Biol. 2022 Apr 4;18(4):e1009973. doi: 10.1371/journal.pcbi.1009973. eCollection 2022 Apr.

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.

摘要

SARS-CoV-2 在更精细的时空尺度上的传播区域差异的驱动因素尚未完全被理解。在这里,我们开发了一种基于年龄结构的房室模型的数据驱动建模方法,该方法将 116 个奥地利地区与一个经过适当选择的控制地区集合进行比较,以通过气象因素、非药物干预措施和流动性的组合来解释当地传播率的变化。我们发现,这些因素可以解释观察到的区域变化的 60%以上。温度和湿度降低、云量增加、降水增加以及没有对公共活动采取缓解措施是导致病毒传播增加的最强驱动因素,这些因素共同导致与天气条件较好的地区相比,传播率增加了一倍。我们推测,对于经历向不利天气条件转变且对大型活动几乎没有缓解措施的地区,它们特别容易成为下一个季节性 SARS-CoV-2 波的核化点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1181/9009775/b35426a39151/pcbi.1009973.g001.jpg

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