气候变化领域集成服务门户(CCPortal): Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries

CCPortal

DOI	10.1073/pnas.2100201118
	Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries
	Han X.; Xu Y.; Fan L.; Huang Y.; Xu M.; Gao S.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:31
英文摘要	Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	COVID-19; Importation risk; Mobility networks; Spatial dynamic panel data model; Spatiotemporal analysis
语种	英语
scopus关键词	analytic method; Article; China; coronavirus disease 2019; disease transmission; geography; health care policy; human; intersectoral collaboration; population dynamics; protocol compliance; quantitative analysis; risk assessment; travel; city; communicable disease; communicable disease control; legislation and jurisprudence; prevention and control; risk; spatiotemporal analysis; statistical model; China; Cities; Communicable Disease Control; Communicable Diseases, Imported; COVID-19; Humans; Models, Statistical; Risk; SARS-CoV-2; Spatio-Temporal Analysis; Travel
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238842
作者单位	Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, Xiamen, 361005, China; School of Economics, Xiamen University, Xiamen, 361005, China; Department of Agriculture and Consumer Economics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United States; Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, Philadelphia, PA 16802, United States; Institute of Urban Development, Nanjing Audit University, Nanjing, 211815, China; Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, United States
推荐引用方式 GB/T 7714	Han X.,Xu Y.,Fan L.,et al. Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries[J],2021,118(31).
APA	Han X.,Xu Y.,Fan L.,Huang Y.,Xu M.,&Gao S..(2021).Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries.Proceedings of the National Academy of Sciences of the United States of America,118(31).
MLA	Han X.,et al."Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries".Proceedings of the National Academy of Sciences of the United States of America 118.31(2021).