Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak
Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak
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DOI：
10.3390/v12060588
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发表时间：
2020-06-01
期刊：
影响因子：
4.7
通讯作者：
Lucic, Bojana
中科院分区：
文献类型：
--
作者：
Fronza, Raffaele;Lusic, Marina;Lucic, Bojana
全球爆发的严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）感染导致2019冠状病毒病（COVID-19），全球确诊病例已超过500万例，且数字仍在快速增长。尽管感染广泛爆发，但在病例数量和患者COVID-19症状严重程度的分布方面，观察到国家/地区之间存在显著的不对称性。在新病原体爆发的早期阶段，了解感染传播的动态至关重要，以便随着时间的推移跟踪传染并预测不久的将来的流行病学状况。虽然可以推断，观察到的病例数量和严重程度的变化源于感染者的初始数量、检测政策和社区传播的社会方面的差异，但可以解释医疗保健水平相似地区高度差异的因素仍然未知。在这里，我们介绍了一种基于人工神经网络的二元分类器，可以帮助解释这些差异，并可用于支持遏制政策的设计。我们发现，SARS-CoV-2感染频率与颗粒空气污染物呈正相关，特别是与颗粒物2.5（PM2.5），而臭氧气体与感染人数呈负相关。因此，我们建议大气污染物可以作为替代标志物，以补充感染爆发的预期。
The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation.