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

Wenzhong Shi et al. propose an extended Weight Kernel Density Estimation model to predict the COVID-19 onset risk, with and without the Wuhan lockdown, and corresponding symptom onset and spatial heterogeneity in 347 Chinese cities. The authors find that the lockdown delayed COVID-19 peak onset by 1...

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Main Authors:	Wenzhong Shi, Chengzhuo Tong, Anshu Zhang, Bin Wang, Zhicheng Shi, Yepeng Yao, Peng Jia
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-01-01
Series:	Communications Biology
Online Access:	https://doi.org/10.1038/s42003-021-01677-2

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spelling	doaj-ad2ac98641494ad49b5bb3bf054219d62021-01-31T16:16:18ZengNature Publishing GroupCommunications Biology2399-36422021-01-014111010.1038/s42003-021-01677-2An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in ChinaWenzhong Shi0Chengzhuo Tong1Anshu Zhang2Bin Wang3Zhicheng Shi4Yepeng Yao5Peng Jia6Smart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic UniversitySmart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic UniversitySmart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic UniversityCollege of Oceanography and Space Informatics, China University of PetroleumResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen UniversitySmart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic UniversitySmart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic UniversityWenzhong Shi et al. propose an extended Weight Kernel Density Estimation model to predict the COVID-19 onset risk, with and without the Wuhan lockdown, and corresponding symptom onset and spatial heterogeneity in 347 Chinese cities. The authors find that the lockdown delayed COVID-19 peak onset by 1–2 days and decreased onset risk by up to 21%.https://doi.org/10.1038/s42003-021-01677-2
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Wenzhong Shi Chengzhuo Tong Anshu Zhang Bin Wang Zhicheng Shi Yepeng Yao Peng Jia
spellingShingle	Wenzhong Shi Chengzhuo Tong Anshu Zhang Bin Wang Zhicheng Shi Yepeng Yao Peng Jia An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China Communications Biology
author_facet	Wenzhong Shi Chengzhuo Tong Anshu Zhang Bin Wang Zhicheng Shi Yepeng Yao Peng Jia
author_sort	Wenzhong Shi
title	An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China
title_short	An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China
title_full	An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China
title_fullStr	An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China
title_full_unstemmed	An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China
title_sort	extended weight kernel density estimation model forecasts covid-19 onset risk and identifies spatiotemporal variations of lockdown effects in china
publisher	Nature Publishing Group
series	Communications Biology
issn	2399-3642
publishDate	2021-01-01
description	Wenzhong Shi et al. propose an extended Weight Kernel Density Estimation model to predict the COVID-19 onset risk, with and without the Wuhan lockdown, and corresponding symptom onset and spatial heterogeneity in 347 Chinese cities. The authors find that the lockdown delayed COVID-19 peak onset by 1–2 days and decreased onset risk by up to 21%.
url	https://doi.org/10.1038/s42003-021-01677-2
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An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

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