4.7 Article

Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 756, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2020.143343

关键词

COVID-19; Two-stage; Environment impact; Interactive effect; GWR model; City-level

资金

  1. National Key Research and Development Program of China [2016YFA0600104]
  2. Fundamental Research Funds for the Central Universities
  3. BNU Interdisciplinary Research Foundation for the First-Year Doctoral Candidates [BNUXKJC2019]

向作者/读者索取更多资源

The study investigates the impact of environmental factors on the spread pattern of COVID-19 in China, highlighting the significance of both natural environment and human activities. The results demonstrate spatial heterogeneity in the effects of various factors on the disease transmission, especially in terms of population flow, climate factors, and urban transportation before and after the lockdown in Wuhan.
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 (12) 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 degrees 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 72 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. (C) 2020 Elsevier B.V. All rights reserved.

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