A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate
A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate
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DOI：
10.1016/j.ecolmodel.2020.109187
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发表时间：
2020-09-01
影响因子：
3.1
通讯作者：
Coro, Gianpaolo
中科院分区：
文献类型：
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作者：
Coro, Gianpaolo
COVID-19大流行对人类健康和经济构成全球性威胁，需要采取紧急预防和监测策略。目前正在研究几种模式，以控制疾病的传播和感染率，并检测可能有利于疾病传播和感染的因素，重点是了解疾病与具体地球物理参数之间的相关性。然而，这一流行病在受感染国家并没有造成明显的环境障碍。然而，部分国家的感染率较低，这可能与特定的人口和气候条件有关。本文采用基于最大熵的生态位模型（Maximum Entropy Based Ecological Niche Model），以0.5度分辨率对全球COVID-19感染率进行建模，该模型确定了可能受到高感染率影响的地理区域。该模型确定了可能有利于感染率的地点，因为它们具有特定的地球物理特征（地表气温、降水和海拔）和与人类相关的特征（二氧化碳和人口密度）。它通过促进来自意大利各省的数据进行培训，这些省份报告了高感染率，随后使用世界各国报告的数据集进行测试。在此基础上，通过计算风险指数，确定了世界上潜在的疾病增长风险高的国家和地区，分布结果预测了许多实际发生疾病暴发的地区的高感染率，例如中国湖北省，并且在大多数已经报告了重大爆发的世界国家（例如美国西部）中报告了疾病增加的高风险。总体而言，研究结果表明，所选参数的复杂组合可能对了解COVID-19在人群中的传播具有重要意义，特别是在欧洲。该模型和数据通过开放科学网络服务分发，以最大限度地提高新数据和新疾病的可重用性，并提高方法和结果的透明度。
COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.In this paper, infection rate of COVID-19 is modelled globally at a 0.5 degrees resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment.The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.