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机器学习表明,在意大利,长时间暴露于空气污染与 SARS-CoV-2 死亡率和传染性有关。

Machine learning reveals that prolonged exposure to air pollution is associated with SARS-CoV-2 mortality and infectivity in Italy.

机构信息

Biological Institute, Tomsk State University, Russia; Konrad Lorenz Institute for Evolution and Cognition Research, Austria.

Biological Institute, Tomsk State University, Russia.

出版信息

Environ Pollut. 2020 Dec;267:115471. doi: 10.1016/j.envpol.2020.115471. Epub 2020 Aug 21.

DOI:10.1016/j.envpol.2020.115471
PMID:32882464
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7442434/
Abstract

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.

摘要

空气污染会增加患呼吸道疾病的风险,使人们更容易感染病毒和细菌。一些研究表明,小的空气颗粒有助于病毒的传播,也有助于新型冠状病毒的传播,除了直接的人际传播。然而,暴露于颗粒物和其他污染物对 SARS-CoV-2 的影响还没有得到充分的研究。在这里,我们研究了为什么新型冠状病毒对意大利地区和省级人群的影响不同。我们利用人工智能研究了空气污染对意大利 SARS-CoV-2 死亡率和阳性率的可能影响。我们发现,在几个环境、健康和社会经济因素中,空气污染和细颗粒物(PM2.5)是其主要成分,是 SARS-CoV-2 影响的最重要预测因素。我们还发现,工业、农场和道路交通的排放物(按重要性排序)可能对全国与 SARS-CoV-2 相关的死亡人数的 70%以上负责。鉴于空气污染的重大贡献(比我们发现的其他健康和社会经济因素更为重要),我们预测,随着空气污染增加 5-10%,类似的未来病原体可能会使意大利的疫情死亡人数增加 21-32%,新增病例中阳性率增加 19-28%,死亡率增加 4-14%。我们的研究结果表明,细颗粒物(PM2.5)污染物水平是预测 SARS-CoV-2 影响的最重要因素,即使空气质量略有下降,也会使影响恶化,这凸显了在健康和环境保护之前追求生产力确实是一种短视/狭隘的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/bfadd96f4bf1/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/4690aa64a482/fx1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/d4eadd51950b/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/2307e73176bc/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/e6f10c58c9f2/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/cba58c2a14f8/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/bfadd96f4bf1/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/4690aa64a482/fx1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/d4eadd51950b/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/2307e73176bc/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/e6f10c58c9f2/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/cba58c2a14f8/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f262/7442434/bfadd96f4bf1/gr5_lrg.jpg

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