An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

Wenzhong Shi, Chengzhuo Tong, Anshu Zhang, Bin Wang, Zhicheng Shi, Yepeng Yao, Peng Jia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

It is important to forecast the risk of COVID-19 symptom onset and thereby evaluate how effectively the city lockdown measure could reduce this risk. This study is a first comprehensive, high-resolution investigation of spatiotemporal heterogeneities on the effect of the Wuhan lockdown on the risk of COVID-19 symptom onset in all 347 Chinese cities. An extended Weight Kernel Density Estimation model was developed to predict the COVID-19 onset risk under two scenarios (i.e., with and without the Wuhan lockdown). The Wuhan lockdown, compared with the scenario without lockdown implementation, in general, delayed the arrival of the COVID-19 onset risk peak for 1–2 days and lowered risk peak values among all cities. The decrease of the onset risk attributed to the lockdown was more than 8% in over 40% of Chinese cities, and up to 21.3% in some cities. Lockdown was the most effective in areas with medium risk before lockdown.

Original languageEnglish
Article number126
JournalCommunications Biology
Volume4
Issue number1
DOIs
Publication statusPublished - Dec 2021

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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