Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China
Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research a...
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| Veröffentlicht in: | International journal of environmental research and public health 2020-04, Vol.17 (7), p.2563 |
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| description | Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic. |
| doi_str_mv | 10.3390/ijerph17072563 |
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However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph17072563</identifier><identifier>PMID: 32276501</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Betacoronavirus ; China - epidemiology ; Coronavirus ; Coronavirus Infections - epidemiology ; Coronaviruses ; COVID-19 ; Datasets ; Disease control ; Disease Outbreaks ; Epidemics ; Humans ; Identification methods ; Incidence ; Metropolitan areas ; Outliers (statistics) ; Pandemics ; Pneumonia, Viral - epidemiology ; SARS-CoV-2 ; Spatial Analysis ; Spatio-Temporal Analysis ; Trends</subject><ispartof>International journal of environmental research and public health, 2020-04, Vol.17 (7), p.2563</ispartof><rights>2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.</description><subject>Betacoronavirus</subject><subject>China - epidemiology</subject><subject>Coronavirus</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Disease control</subject><subject>Disease Outbreaks</subject><subject>Epidemics</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Incidence</subject><subject>Metropolitan areas</subject><subject>Outliers (statistics)</subject><subject>Pandemics</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>SARS-CoV-2</subject><subject>Spatial Analysis</subject><subject>Spatio-Temporal Analysis</subject><subject>Trends</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkc1r3DAQxUVpaD7aa49B0EsPdaovy_alEMw2G1hooJtchSyPs1psyZXkhf3v62STkOQ0A_Obx3s8hL5ScsF5RX7aLYRxQwtSsFzyD-iESkkyIQn9-Go_RqcxbgnhpZDVJ3TMGStkTugJUn9HnazP1jCMPuge3-iUILiIfYfTBjAjtMpc7e_wYrQtDNZgnR4vtZ9c2uMV7KDH1uHl1IDFN8HvrDPwA9cb6_RndNTpPsKXp3mGbn8v1vUyW_25uq4vV5kRtEwZ450QXdMwkBWjMoeWGpHnxORN2dLOMEkhbxttSCuLjjPBGt4yVhasoQIM4Wfo10F3nJoBWgMuzWnUGOygw155bdXbi7Mbde93qqBFwQWdBb4_CQT_b4KY1GCjgb7XDvwUFeNlWTLC6QP67R269VNwc7xHqhJsNjhTFwfKBB9jgO7FDCXqoTv1trv54fx1hBf8uSz-H9btlOw</recordid><startdate>20200408</startdate><enddate>20200408</enddate><creator>Yang, Wentao</creator><creator>Deng, Min</creator><creator>Li, Chaokui</creator><creator>Huang, Jincai</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200408</creationdate><title>Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China</title><author>Yang, Wentao ; Deng, Min ; Li, Chaokui ; Huang, Jincai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-23f44fbb2e692165ed1c4550c5b8d1fc261e5dbac0d67f3242b3d22872b14ec03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Betacoronavirus</topic><topic>China - epidemiology</topic><topic>Coronavirus</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Disease control</topic><topic>Disease Outbreaks</topic><topic>Epidemics</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Incidence</topic><topic>Metropolitan areas</topic><topic>Outliers (statistics)</topic><topic>Pandemics</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>SARS-CoV-2</topic><topic>Spatial Analysis</topic><topic>Spatio-Temporal Analysis</topic><topic>Trends</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Wentao</creatorcontrib><creatorcontrib>Deng, Min</creatorcontrib><creatorcontrib>Li, Chaokui</creatorcontrib><creatorcontrib>Huang, Jincai</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Wentao</au><au>Deng, Min</au><au>Li, Chaokui</au><au>Huang, Jincai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2020-04-08</date><risdate>2020</risdate><volume>17</volume><issue>7</issue><spage>2563</spage><pages>2563-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. 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| subjects | Betacoronavirus China - epidemiology Coronavirus Coronavirus Infections - epidemiology Coronaviruses COVID-19 Datasets Disease control Disease Outbreaks Epidemics Humans Identification methods Incidence Metropolitan areas Outliers (statistics) Pandemics Pneumonia, Viral - epidemiology SARS-CoV-2 Spatial Analysis Spatio-Temporal Analysis Trends |
| title | Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China |
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