State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2021 Feb 20;756:143343. doi: 10.1016/j.scitotenv.2020.143343. Epub 2020 Oct 29.
A novel Coronavirus COVID-19 has caused high morbidity and mortality in China and worldwide. A few studies have explored the impact of climate change or human activity on the disease incidence in China or a city. The integrated study concerning environment impact on the emerging disease is rarely reported. Therefore, based on the two-stage modeling study, we investigate the effect of both natural and human environment on COVID-19 incidence at a city level. Besides, the interactive effect of different factors on COVID-19 incidence is analyzed using Geodetector; the impact of effective factors and interaction terms on COVID-19 is simulated with Geographically Weighted Regression (GWR) models. The results find that mean temperature (MeanT), destination proportion in population flow from Wuhan (WH), migration scale (MS), and WH*MeanT, are generally promoting for COVID-19 incidence before Wuhan's shutdown (T1); the WH and MeanT play a determinant role in the disease spread in T1. The effect of environment on COVID-19 incidence after Wuhan's shutdown (T2) includes more factors (including mean DEM, relative humidity, precipitation (Pre), travel intensity within a city (TC), and their interactive terms) than T1, and their effect shows distinct spatial heterogeneity. Interestingly, the dividing line of positive-negative effect of MeanT and Pre on COVID-19 incidence is 8.5°C and 1 mm, respectively. In T2, WH has weak impact, but the MS has the strongest effect. The COVID-19 incidence in T2 without quarantine is also modeled using the developed GWR model, and the modeled incidence shows an obvious increase for 75.6% cities compared with reported incidence in T2 especially for some mega cities. This evidences national quarantine and traffic control take determinant role in controlling the disease spread. The study indicates that both natural environment and human factors integratedly affect the spread pattern of COVID-19 in China.
一种新型冠状病毒 COVID-19 在中国和全球范围内造成了高发病率和死亡率。有一些研究探讨了气候变化或人类活动对中国或一个城市疾病发病率的影响。关于环境对新发疾病影响的综合研究很少有报道。因此，基于两阶段建模研究，我们调查了自然和人为环境对城市层面 COVID-19 发病率的影响。此外，使用地理探测器分析了不同因素对 COVID-19 发病率的交互影响；使用地理加权回归（GWR）模型模拟了有效因素和交互项对 COVID-19 的影响。结果发现，平均温度（MeanT）、武汉人口流动目的地比例（WH）、移民规模（MS）和 WH*MeanT 对武汉关闭前（T1）的 COVID-19 发病率普遍有促进作用；武汉和 MeanT 在 T1 中对疾病传播起决定性作用。武汉关闭后（T2）环境对 COVID-19 发病率的影响包括更多因素（包括平均 DEM、相对湿度、降水（Pre）、市内旅行强度（TC）及其交互项），其影响表现出明显的空间异质性。有趣的是，MeanT 和 Pre 对 COVID-19 发病率的正负影响的分界线分别为 8.5°C 和 1mm。在 T2 中，武汉的影响较弱，但 MS 的影响最大。在没有隔离的情况下，使用开发的 GWR 模型对 T2 中的 COVID-19 发病率进行建模，与 T2 中的报告发病率相比，75.6%的城市的建模发病率明显增加，尤其是一些特大城市。这证明了全国性的隔离和交通管制在控制疾病传播方面起着决定性的作用。该研究表明，自然环境和人为因素综合影响了 COVID-19 在我国的传播模式。
