Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China
The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its corre...
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| Published in | Chinese medical journal Vol. 133; no. 9; pp. 1044 - 1050 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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China
The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license
05.05.2020
Lippincott Williams & Wilkins Ovid Technologies Wolters Kluwer Health Wolters Kluwer |
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| Abstract | The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks.
The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed.
The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases.
The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks. |
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| AbstractList | Background. The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks. Methods. The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. Results. The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases. Conclusions. The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks. The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks.BACKGROUNDThe ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks.The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed.METHODSThe official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed.The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases.RESULTSThe COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases.The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks.CONCLUSIONSThe population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks. BackgroundThe ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks.MethodsThe official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed.ResultsThe COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases.ConclusionsThe population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks. The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks. The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases. The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks. |
| Author | Guo, Zhong-Min Guo, Cheng Zhang, Qi Han, Xiao-Hu Chen, Ze-Liang Li, Qian-Lin Liao, Cong-Hui Lu, Yi Zhang, Xi Lu, Jia-Hai Zhang, Wen-Jun |
| AuthorAffiliation | One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA Animal Experiment Center, Sun Yat-sen University, Guangzhou, Guangdong 510080, China College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, Liaoning 110866, China Department of Health Law, Policy and Management, School of Public Health, Boston University, Boston, MA 02215, USA Key Laboratory of Livestock Infectious Diseases in Northeast China, Ministry of Education, Shenyang Agricultural University, Shenyang, Liaoning 110866, China Department of Biological Science and Technology, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510080, China |
| AuthorAffiliation_xml | – name: Department of Biological Science and Technology, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – name: Key Laboratory of Livestock Infectious Diseases in Northeast China, Ministry of Education, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – name: College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – name: Department of Health Law, Policy and Management, School of Public Health, Boston University, Boston, MA 02215, USA – name: Animal Experiment Center, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – name: Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA – name: One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – name: 3 College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – name: 6 Department of Biological Science and Technology, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – name: 4 Department of Health Law, Policy and Management, School of Public Health, Boston University, Boston, MA 02215, USA – name: 2 Key Laboratory of Livestock Infectious Diseases in Northeast China, Ministry of Education, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – name: 5 Animal Experiment Center, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – name: 7 Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA – name: 1 One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China |
| Author_xml | – sequence: 1 givenname: Ze-Liang surname: Chen fullname: Chen, Ze-Liang organization: One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – sequence: 2 givenname: Qi surname: Zhang fullname: Zhang, Qi organization: College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – sequence: 3 givenname: Yi surname: Lu fullname: Lu, Yi organization: Department of Health Law, Policy and Management, School of Public Health, Boston University, Boston, MA 02215, USA – sequence: 4 givenname: Zhong-Min surname: Guo fullname: Guo, Zhong-Min organization: Animal Experiment Center, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – sequence: 5 givenname: Xi surname: Zhang fullname: Zhang, Xi organization: College of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – sequence: 6 givenname: Wen-Jun surname: Zhang fullname: Zhang, Wen-Jun organization: Department of Biological Science and Technology, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – sequence: 7 givenname: Cheng surname: Guo fullname: Guo, Cheng organization: Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA – sequence: 8 givenname: Cong-Hui surname: Liao fullname: Liao, Cong-Hui organization: One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – sequence: 9 givenname: Qian-Lin surname: Li fullname: Li, Qian-Lin organization: One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China – sequence: 10 givenname: Xiao-Hu surname: Han fullname: Han, Xiao-Hu organization: Key Laboratory of Livestock Infectious Diseases in Northeast China, Ministry of Education, Shenyang Agricultural University, Shenyang, Liaoning 110866, China – sequence: 11 givenname: Jia-Hai surname: Lu fullname: Lu, Jia-Hai organization: One Health Center, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32118644$$D View this record in MEDLINE/PubMed |
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| Copyright | The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. Copyright © 2020 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2020 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. 2020 |
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| References_xml | – volume: 201 start-page: 7 year: 2020 ident: R7-20230813 article-title: Novel Wuhan (2019-nCoV) coronavirus publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.2014P7 – year: 2020 ident: R10-20230813 article-title: Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia publication-title: N Engl J Med doi: 10.1056/NEJMoa2001316 – volume: 310 start-page: 676 year: 2005 ident: R3-20230813 article-title: Bats are natural reservoirs of SARS-like coronaviruses publication-title: Science doi: 10.1126/science.1118391 – volume: 17 start-page: 449 year: 2019 ident: R4-20230813 article-title: Origin of viruses: primordial replicators recruiting capsids from hosts publication-title: Nat Rev Microbiol doi: 10.1038/s41579-019-0205-6 – volume: 382 start-page: 929 year: 2020 ident: R5-20230813 article-title: First case of 2019 novel coronavirus in the United States publication-title: N Engl J Med doi: 10.1056/NEJMoa2001191 – volume: 19 start-page: 2333 year: 2000 ident: R11-20230813 article-title: Space-time mixture modelling of public health data publication-title: Stat Med doi: 10.1002/1097-0258(20000915/30)19:17/18<2333::AID-SIM573>3.0.CO;2-Q – volume: 382 start-page: 727 year: 2020 ident: R6-20230813 article-title: A novel coronavirus from patients with pneumonia in China, 2019 publication-title: N Engl J Med doi: 10.1056/NEJMoa2001017 – volume: 11 start-page: e0150332 year: 2016 ident: R12-20230813 article-title: Temporal and Spatial Analysis of Neural Tube Defects and Detection of Geographical Factors in Shanxi Province, China publication-title: PloS one doi: 10.1371/journal.pone.0150332 – volume: 9 start-page: 1453 year: 2003 ident: R20-20230813 article-title: Severe acute respiratory syndrome-associated coronavirus infection publication-title: Emerg Infect Dis doi: 10.3201/eid0911.030421 – volume: 112 start-page: 1016 year: 2004 ident: R15-20230813 article-title: Interpreting posterior relative risk estimates in disease-mapping studies publication-title: Environ Health Perspect doi: 10.1289/ehp.6740 – volume: 395 start-page: 311 year: 2020 ident: R9-20230813 article-title: Emerging understandings of 2019-nCoV publication-title: Lancet doi: 10.1016/S0140-6736(20)30186-0 – year: 2020 ident: R8-20230813 article-title: Drug treatment options for the 2019-new coronavirus (2019-nCoV) publication-title: Biosci Trends doi: 10.5582/bst.2020.01020 – volume: 28 start-page: 349 year: 2015 ident: R2-20230813 article-title: Risks to healthcare workers with emerging diseases: lessons from MERS-CoV, Ebola, SARS, and avian flu publication-title: Curr Opin Infect Dis doi: 10.1097/QCO.0000000000000183 – volume: 135 start-page: 163 year: 2015 ident: R13-20230813 article-title: Joint prior distributions for variance parameters in Bayesian analysis of normal hierarchical models publication-title: J Multivar Anal doi: 10.1016/j.jmva.2014.12.013 – volume: 430 start-page: 242 year: 2004 ident: R1-20230813 article-title: The challenge of emerging and re-emerging infectious diseases publication-title: Nature doi: 10.1038/nature02759 – year: 2020 ident: R18-20230813 article-title: Preparedness and proactive infection control measures against the emerging Wuhan coronavirus pneumonia in China publication-title: J Hosp Infect doi: 10.1016/j.jhin.2020.01.010 – volume: 9 year: 2020 ident: R16-20230813 article-title: The extent of transmission of novel coronavirus in Wuhan, China, 2020 publication-title: J Clin Med doi: 10.3390/jcm9020330 – volume: 92 start-page: 214 year: 2020 ident: R22-20230813 article-title: Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak publication-title: Int J Infect Dis doi: 10.1016/j.ijid.2020.01.050 – volume: 92 start-page: 441 year: 2020 ident: R17-20230813 article-title: Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China publication-title: J Med Virol doi: 10.1002/jmv.25689 – volume: 43 start-page: 1 year: 1991 ident: R14-20230813 article-title: Bayesian image restoration, with two applications in spatial statistics publication-title: Ann Inst Stat Math doi: 10.1007/BF00116466 |
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| SubjectTerms | Betacoronavirus China - epidemiology Cities Coronavirus Infections - epidemiology Coronaviruses COVID-19 Disease prevention Emigration Emigration and Immigration Epidemics Humans Infectious diseases Laboratories Original Pandemics Pneumonia Pneumonia, Viral - epidemiology Provinces SARS-CoV-2 Software Spacetime Trends |
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| Title | Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China |
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