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

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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Autores principales: Wenzhong Shi, Chengzhuo Tong, Anshu Zhang, Bin Wang, Zhicheng Shi, Yepeng Yao, Peng Jia
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Biology (General)
QH301-705.5
Acceso en línea:https://doaj.org/article/ad2ac98641494ad49b5bb3bf054219d6
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spelling oai:doaj.org-article:ad2ac98641494ad49b5bb3bf054219d62021-12-02T14:16:33ZAn extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China10.1038/s42003-021-01677-22399-3642https://doaj.org/article/ad2ac98641494ad49b5bb3bf054219d62021-01-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-01677-2https://doaj.org/toc/2399-3642Wenzhong 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%.Wenzhong ShiChengzhuo TongAnshu ZhangBin WangZhicheng ShiYepeng YaoPeng JiaNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-10 (2021)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
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
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%.
format article
author Wenzhong Shi
Chengzhuo Tong
Anshu Zhang
Bin Wang
Zhicheng Shi
Yepeng Yao
Peng Jia
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 Portfolio
publishDate 2021
url https://doaj.org/article/ad2ac98641494ad49b5bb3bf054219d6
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