Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020
People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns. This study offers a framework for evaluating interactions among individuals' emotions, percepti...
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| Veröffentlicht in: | China CDC weekly 2023-01, Vol.5 (4), p.71-75 |
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| container_title | China CDC weekly |
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| creator | Du, Zhanwei Zhang, Xiao Wang, Lin Yao, Sidan Bai, Yuan Tan, Qi Xu, Xiaoke Pei, Sen Xiao, Jingyi Tsang, Tim K Liao, Qiuyan Lau, Eric H Y Wu, Peng Gao, Chao Cowling, Benjamin J |
| description | People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns.
This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time.
Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives. |
| doi_str_mv | 10.46234/ccdcw2023.014 |
| format | Article |
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This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time.
Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.</description><identifier>ISSN: 2096-7071</identifier><identifier>EISSN: 2096-7071</identifier><identifier>DOI: 10.46234/ccdcw2023.014</identifier><identifier>PMID: 36777899</identifier><language>eng</language><publisher>China</publisher><ispartof>China CDC weekly, 2023-01, Vol.5 (4), p.71-75</ispartof><rights>Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2023.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c335t-3882e40d3032ec6acf6b88ebe16ba208b651683486abffc5e2705a67f7ef5f843</citedby><cites>FETCH-LOGICAL-c335t-3882e40d3032ec6acf6b88ebe16ba208b651683486abffc5e2705a67f7ef5f843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36777899$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Du, Zhanwei</creatorcontrib><creatorcontrib>Zhang, Xiao</creatorcontrib><creatorcontrib>Wang, Lin</creatorcontrib><creatorcontrib>Yao, Sidan</creatorcontrib><creatorcontrib>Bai, Yuan</creatorcontrib><creatorcontrib>Tan, Qi</creatorcontrib><creatorcontrib>Xu, Xiaoke</creatorcontrib><creatorcontrib>Pei, Sen</creatorcontrib><creatorcontrib>Xiao, Jingyi</creatorcontrib><creatorcontrib>Tsang, Tim K</creatorcontrib><creatorcontrib>Liao, Qiuyan</creatorcontrib><creatorcontrib>Lau, Eric H Y</creatorcontrib><creatorcontrib>Wu, Peng</creatorcontrib><creatorcontrib>Gao, Chao</creatorcontrib><creatorcontrib>Cowling, Benjamin J</creatorcontrib><creatorcontrib>Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore</creatorcontrib><creatorcontrib>School of Artificial Intelligence, Optics, and Electronics (iOpen), Northwestern Polytechnical University, Xi’an City, Shaanxi Province, China</creatorcontrib><creatorcontrib>Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK</creatorcontrib><creatorcontrib>College of Information and Communication Engineering, Dalian Minzu University, Dalian City, Liaoning Province, China</creatorcontrib><creatorcontrib>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China</creatorcontrib><creatorcontrib>Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China</creatorcontrib><creatorcontrib>Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA</creatorcontrib><title>Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020</title><title>China CDC weekly</title><addtitle>China CDC Wkly</addtitle><description>People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns.
This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time.
Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.</description><issn>2096-7071</issn><issn>2096-7071</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpNkEtPAjEURhujEYJsXZouXTDYaaePWeKgQiQh8cF20im3MmYe2DIY_fUOgsTNfSTnfrk5CF2GZBgJyqIbY5bmkxLKhiSMTlCXklgEksjw9N_cQX3v3wkhNKaUKnGOOkxIKVUcd9EiWWmnzQZc_p1Xb3jSlLrCSV0UYDb5FvAtrPQ2r53H48btiGS-mI6DMMYBntTt_rgrz6OnAU5WeaUHuP2HXKAzqwsP_UPvodf7u5dkEszmD9NkNAsMY3wTMKUoRGTJCKNghDZWZEpBBqHINCUqEzwUikVK6Mxaw4FKwrWQVoLlVkWsh673uWtXfzTgN2mZewNFoSuoG59SKXnMuSKkRYd71Ljaewc2Xbu81O4rDUn6qzM96kxbne3B1SG7yUpYHvE_eewHz2JtnQ</recordid><startdate>20230127</startdate><enddate>20230127</enddate><creator>Du, Zhanwei</creator><creator>Zhang, Xiao</creator><creator>Wang, Lin</creator><creator>Yao, Sidan</creator><creator>Bai, Yuan</creator><creator>Tan, Qi</creator><creator>Xu, Xiaoke</creator><creator>Pei, Sen</creator><creator>Xiao, Jingyi</creator><creator>Tsang, Tim K</creator><creator>Liao, Qiuyan</creator><creator>Lau, Eric H Y</creator><creator>Wu, Peng</creator><creator>Gao, Chao</creator><creator>Cowling, Benjamin J</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20230127</creationdate><title>Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020</title><author>Du, Zhanwei ; Zhang, Xiao ; Wang, Lin ; Yao, Sidan ; Bai, Yuan ; Tan, Qi ; Xu, Xiaoke ; Pei, Sen ; Xiao, Jingyi ; Tsang, Tim K ; Liao, Qiuyan ; Lau, Eric H Y ; Wu, Peng ; Gao, Chao ; Cowling, Benjamin J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c335t-3882e40d3032ec6acf6b88ebe16ba208b651683486abffc5e2705a67f7ef5f843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Du, Zhanwei</creatorcontrib><creatorcontrib>Zhang, Xiao</creatorcontrib><creatorcontrib>Wang, Lin</creatorcontrib><creatorcontrib>Yao, Sidan</creatorcontrib><creatorcontrib>Bai, Yuan</creatorcontrib><creatorcontrib>Tan, Qi</creatorcontrib><creatorcontrib>Xu, Xiaoke</creatorcontrib><creatorcontrib>Pei, Sen</creatorcontrib><creatorcontrib>Xiao, Jingyi</creatorcontrib><creatorcontrib>Tsang, Tim K</creatorcontrib><creatorcontrib>Liao, Qiuyan</creatorcontrib><creatorcontrib>Lau, Eric H Y</creatorcontrib><creatorcontrib>Wu, Peng</creatorcontrib><creatorcontrib>Gao, Chao</creatorcontrib><creatorcontrib>Cowling, Benjamin J</creatorcontrib><creatorcontrib>Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore</creatorcontrib><creatorcontrib>School of Artificial Intelligence, Optics, and Electronics (iOpen), Northwestern Polytechnical University, Xi’an City, Shaanxi Province, China</creatorcontrib><creatorcontrib>Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK</creatorcontrib><creatorcontrib>College of Information and Communication Engineering, Dalian Minzu University, Dalian City, Liaoning Province, China</creatorcontrib><creatorcontrib>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China</creatorcontrib><creatorcontrib>Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China</creatorcontrib><creatorcontrib>Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>China CDC weekly</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Du, Zhanwei</au><au>Zhang, Xiao</au><au>Wang, Lin</au><au>Yao, Sidan</au><au>Bai, Yuan</au><au>Tan, Qi</au><au>Xu, Xiaoke</au><au>Pei, Sen</au><au>Xiao, Jingyi</au><au>Tsang, Tim K</au><au>Liao, Qiuyan</au><au>Lau, Eric H Y</au><au>Wu, Peng</au><au>Gao, Chao</au><au>Cowling, Benjamin J</au><aucorp>Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore</aucorp><aucorp>School of Artificial Intelligence, Optics, and Electronics (iOpen), Northwestern Polytechnical University, Xi’an City, Shaanxi Province, China</aucorp><aucorp>Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK</aucorp><aucorp>College of Information and Communication Engineering, Dalian Minzu University, Dalian City, Liaoning Province, China</aucorp><aucorp>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China</aucorp><aucorp>Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China</aucorp><aucorp>Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020</atitle><jtitle>China CDC weekly</jtitle><addtitle>China CDC Wkly</addtitle><date>2023-01-27</date><risdate>2023</risdate><volume>5</volume><issue>4</issue><spage>71</spage><epage>75</epage><pages>71-75</pages><issn>2096-7071</issn><eissn>2096-7071</eissn><abstract>People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns.
This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time.
Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.</abstract><cop>China</cop><pmid>36777899</pmid><doi>10.46234/ccdcw2023.014</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| title | Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020 |
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