Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China
Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effe...
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Veröffentlicht in: | Applied geography (Sevenoaks) 2022-06, Vol.143, p.102702-102702, Article 102702 |
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description | Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
•The spatio-temporal simulation method based on available big data and gravity model well presented the process of COVID-19.•Four kinds of transmission patterns were identified and they were highly dependent on the urban spatial structure and location.•Location-based precise intervention measures should be implemented according to different regions.•The approach can be used by policy-makers to make rapid and accurate risk assessment and to implement intervention ahead of epidemic outbreaks. |
doi_str_mv | 10.1016/j.apgeog.2022.102702 |
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•The spatio-temporal simulation method based on available big data and gravity model well presented the process of COVID-19.•Four kinds of transmission patterns were identified and they were highly dependent on the urban spatial structure and location.•Location-based precise intervention measures should be implemented according to different regions.•The approach can be used by policy-makers to make rapid and accurate risk assessment and to implement intervention ahead of epidemic outbreaks.</description><identifier>ISSN: 0143-6228</identifier><identifier>EISSN: 1873-7730</identifier><identifier>EISSN: 0143-6228</identifier><identifier>DOI: 10.1016/j.apgeog.2022.102702</identifier><identifier>PMID: 35469327</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Big data ; China ; COVID-19 ; COVID-19 infection ; data collection ; geography ; Gravity mode ; Precise intervention measures ; risk ; Risk assessment ; Spatio-temporal spreading process ; travel</subject><ispartof>Applied geography (Sevenoaks), 2022-06, Vol.143, p.102702-102702, Article 102702</ispartof><rights>2022 The Authors</rights><rights>2022 The Authors.</rights><rights>2022 The Authors 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-9c8e4ec4fd967df72fbf66a3c96a9b8904aa46bdd16985c452aa7611ea5a939b3</citedby><cites>FETCH-LOGICAL-c426t-9c8e4ec4fd967df72fbf66a3c96a9b8904aa46bdd16985c452aa7611ea5a939b3</cites><orcidid>0000-0001-8532-5581 ; 0000-0002-4899-8792</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S014362282200073X$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35469327$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhou, Shuli</creatorcontrib><creatorcontrib>Zhou, Suhong</creatorcontrib><creatorcontrib>Zheng, Zhong</creatorcontrib><creatorcontrib>Lu, Junwen</creatorcontrib><creatorcontrib>Song, Tie</creatorcontrib><title>Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China</title><title>Applied geography (Sevenoaks)</title><addtitle>Appl Geogr</addtitle><description>Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
•The spatio-temporal simulation method based on available big data and gravity model well presented the process of COVID-19.•Four kinds of transmission patterns were identified and they were highly dependent on the urban spatial structure and location.•Location-based precise intervention measures should be implemented according to different regions.•The approach can be used by policy-makers to make rapid and accurate risk assessment and to implement intervention ahead of epidemic outbreaks.</description><subject>Big data</subject><subject>China</subject><subject>COVID-19</subject><subject>COVID-19 infection</subject><subject>data collection</subject><subject>geography</subject><subject>Gravity mode</subject><subject>Precise intervention measures</subject><subject>risk</subject><subject>Risk assessment</subject><subject>Spatio-temporal spreading process</subject><subject>travel</subject><issn>0143-6228</issn><issn>1873-7730</issn><issn>0143-6228</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqNUsFu1DAQjRCIlsIfIOQjB7LYju3EHJCqpZRKlSoh4GpN7MmulyQOdrISfB8fhpctBS6Ik6WZN2_eG7-ieMroilGmXu5WMG0wbFaccp5LvKb8XnHKmroq67qi94tTykRVKs6bk-JRSjtKqZCSPSxOKimUrnh9Wnx_79NnAilhSgOOM-lCJFNE6xMSP84Y97nqw0hCR9Y3n67elEwTnLzDwVvSQkJHchf24HtoeySt3xAHMxAYHUkT5OFyxmEKEXqS_LD08JNvwHkb3CtyMUw-epubuM-so0XSxTAQ57sO40HStLR93jX1YDFlUeRygXHzbRuWF2S99SM8Lh500Cd8cvueFR_fXnxYvyuvby6v1ufXpRVczaW2DQq0onNa1a6redd2SkFltQLdNpoKAKFa55jSjbRCcoBaMYYgQVe6rc6K10ferGhAZ7O4bMpM0Q8Qv5oA3vzdGf3WbMLeaMqpaJpM8PyWIIYvC6bZDD5Z7HsYMSzJcKWorrVW8j-gUkolmKwzVByhNoaUInZ3ihg1h6yYnTlmxRyyYo5ZyWPP_nRzN_QrHL_tYr7p3mM0yfrDBzmfAzIbF_y_N_wARXjW6A</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Zhou, Shuli</creator><creator>Zhou, Suhong</creator><creator>Zheng, Zhong</creator><creator>Lu, Junwen</creator><creator>Song, Tie</creator><general>Elsevier Ltd</general><general>The Authors. Published by Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8532-5581</orcidid><orcidid>https://orcid.org/0000-0002-4899-8792</orcidid></search><sort><creationdate>20220601</creationdate><title>Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China</title><author>Zhou, Shuli ; Zhou, Suhong ; Zheng, Zhong ; Lu, Junwen ; Song, Tie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-9c8e4ec4fd967df72fbf66a3c96a9b8904aa46bdd16985c452aa7611ea5a939b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Big data</topic><topic>China</topic><topic>COVID-19</topic><topic>COVID-19 infection</topic><topic>data collection</topic><topic>geography</topic><topic>Gravity mode</topic><topic>Precise intervention measures</topic><topic>risk</topic><topic>Risk assessment</topic><topic>Spatio-temporal spreading process</topic><topic>travel</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Shuli</creatorcontrib><creatorcontrib>Zhou, Suhong</creatorcontrib><creatorcontrib>Zheng, Zhong</creatorcontrib><creatorcontrib>Lu, Junwen</creatorcontrib><creatorcontrib>Song, Tie</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Applied geography (Sevenoaks)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Shuli</au><au>Zhou, Suhong</au><au>Zheng, Zhong</au><au>Lu, Junwen</au><au>Song, Tie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China</atitle><jtitle>Applied geography (Sevenoaks)</jtitle><addtitle>Appl Geogr</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>143</volume><spage>102702</spage><epage>102702</epage><pages>102702-102702</pages><artnum>102702</artnum><issn>0143-6228</issn><eissn>1873-7730</eissn><eissn>0143-6228</eissn><abstract>Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
•The spatio-temporal simulation method based on available big data and gravity model well presented the process of COVID-19.•Four kinds of transmission patterns were identified and they were highly dependent on the urban spatial structure and location.•Location-based precise intervention measures should be implemented according to different regions.•The approach can be used by policy-makers to make rapid and accurate risk assessment and to implement intervention ahead of epidemic outbreaks.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>35469327</pmid><doi>10.1016/j.apgeog.2022.102702</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-8532-5581</orcidid><orcidid>https://orcid.org/0000-0002-4899-8792</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Big data China COVID-19 COVID-19 infection data collection geography Gravity mode Precise intervention measures risk Risk assessment Spatio-temporal spreading process travel |
title | Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China |
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