Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries
Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus dise...
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| Veröffentlicht in: | PloS one 2022-08, Vol.17 (8), p.e0272546-e0272546 |
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| creator | Peng, Hsiao-Ya Lin, Yen-Kuang Nguyen, Phung-Anh Hsu, Jason C Chou, Chun-Liang Chang, Chih-Cheng Lin, Chia-Chi Lam, Carlos Chen, Chang-I Wang, Kai-Hsun Lu, Christine Y |
| description | Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies. |
| doi_str_mv | 10.1371/journal.pone.0272546 |
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The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0272546</identifier><identifier>PMID: 36018862</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; Algorithms ; Artificial intelligence ; Artificial neural networks ; Big Data ; Biology and Life Sciences ; Classification ; Computer and Information Sciences ; Coronavirus infections ; Coronaviruses ; COVID-19 ; Cystic fibrosis ; Datasets ; Deep learning ; Diagnosis ; Disease control ; Disease prevention ; Disease transmission ; Epilepsy ; Infections ; Learning algorithms ; Lifestyles ; Machine learning ; Medical research ; Medicine and Health Sciences ; Modules ; Neural networks ; Nosocomial infections ; Olfaction ; Pandemics ; Prevention ; Questionnaires ; Regression analysis ; Severe acute respiratory syndrome coronavirus 2 ; Signs and symptoms ; Smell ; Social Sciences ; Support vector machines ; Technology ; Variables ; Viral diseases</subject><ispartof>PloS one, 2022-08, Vol.17 (8), p.e0272546-e0272546</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Peng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Peng et al 2022 Peng et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c618t-9d0df927a86ce333e212fad6182f833d8c7733a7420865fe011905253ed1ac5b3</cites><orcidid>0000-0002-8051-7485 ; 0000-0002-8710-2338 ; 0000-0003-3958-8342</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9417026/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9417026/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids></links><search><creatorcontrib>Peng, Hsiao-Ya</creatorcontrib><creatorcontrib>Lin, Yen-Kuang</creatorcontrib><creatorcontrib>Nguyen, Phung-Anh</creatorcontrib><creatorcontrib>Hsu, Jason C</creatorcontrib><creatorcontrib>Chou, Chun-Liang</creatorcontrib><creatorcontrib>Chang, Chih-Cheng</creatorcontrib><creatorcontrib>Lin, Chia-Chi</creatorcontrib><creatorcontrib>Lam, Carlos</creatorcontrib><creatorcontrib>Chen, Chang-I</creatorcontrib><creatorcontrib>Wang, Kai-Hsun</creatorcontrib><creatorcontrib>Lu, Christine Y</creatorcontrib><title>Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries</title><title>PloS one</title><description>Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.</description><subject>Acquired immune deficiency syndrome</subject><subject>AIDS</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Big Data</subject><subject>Biology and Life Sciences</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>Coronavirus infections</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Cystic fibrosis</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Disease transmission</subject><subject>Epilepsy</subject><subject>Infections</subject><subject>Learning algorithms</subject><subject>Lifestyles</subject><subject>Machine learning</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Nosocomial infections</subject><subject>Olfaction</subject><subject>Pandemics</subject><subject>Prevention</subject><subject>Questionnaires</subject><subject>Regression analysis</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Signs and symptoms</subject><subject>Smell</subject><subject>Social Sciences</subject><subject>Support vector machines</subject><subject>Technology</subject><subject>Variables</subject><subject>Viral 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of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries</title><author>Peng, Hsiao-Ya ; Lin, Yen-Kuang ; Nguyen, Phung-Anh ; Hsu, Jason C ; Chou, Chun-Liang ; Chang, Chih-Cheng ; Lin, Chia-Chi ; Lam, Carlos ; Chen, Chang-I ; Wang, Kai-Hsun ; Lu, Christine Y</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c618t-9d0df927a86ce333e212fad6182f833d8c7733a7420865fe011905253ed1ac5b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>AIDS</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Big Data</topic><topic>Biology and Life Sciences</topic><topic>Classification</topic><topic>Computer and Information Sciences</topic><topic>Coronavirus infections</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Cystic fibrosis</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Disease transmission</topic><topic>Epilepsy</topic><topic>Infections</topic><topic>Learning algorithms</topic><topic>Lifestyles</topic><topic>Machine learning</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Modules</topic><topic>Neural networks</topic><topic>Nosocomial infections</topic><topic>Olfaction</topic><topic>Pandemics</topic><topic>Prevention</topic><topic>Questionnaires</topic><topic>Regression analysis</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Signs and symptoms</topic><topic>Smell</topic><topic>Social Sciences</topic><topic>Support vector machines</topic><topic>Technology</topic><topic>Variables</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Hsiao-Ya</au><au>Lin, Yen-Kuang</au><au>Nguyen, Phung-Anh</au><au>Hsu, Jason C</au><au>Chou, Chun-Liang</au><au>Chang, Chih-Cheng</au><au>Lin, Chia-Chi</au><au>Lam, Carlos</au><au>Chen, Chang-I</au><au>Wang, Kai-Hsun</au><au>Lu, Christine Y</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries</atitle><jtitle>PloS one</jtitle><date>2022-08-26</date><risdate>2022</risdate><volume>17</volume><issue>8</issue><spage>e0272546</spage><epage>e0272546</epage><pages>e0272546-e0272546</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>36018862</pmid><doi>10.1371/journal.pone.0272546</doi><tpages>e0272546</tpages><orcidid>https://orcid.org/0000-0002-8051-7485</orcidid><orcidid>https://orcid.org/0000-0002-8710-2338</orcidid><orcidid>https://orcid.org/0000-0003-3958-8342</orcidid><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Acquired immune deficiency syndrome AIDS Algorithms Artificial intelligence Artificial neural networks Big Data Biology and Life Sciences Classification Computer and Information Sciences Coronavirus infections Coronaviruses COVID-19 Cystic fibrosis Datasets Deep learning Diagnosis Disease control Disease prevention Disease transmission Epilepsy Infections Learning algorithms Lifestyles Machine learning Medical research Medicine and Health Sciences Modules Neural networks Nosocomial infections Olfaction Pandemics Prevention Questionnaires Regression analysis Severe acute respiratory syndrome coronavirus 2 Signs and symptoms Smell Social Sciences Support vector machines Technology Variables Viral diseases |
| title | Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T10%3A36%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Determinants%20of%20coronavirus%20disease%202019%20infection%20by%20artificial%20intelligence%20technology:%20A%20study%20of%2028%20countries&rft.jtitle=PloS%20one&rft.au=Peng,%20Hsiao-Ya&rft.date=2022-08-26&rft.volume=17&rft.issue=8&rft.spage=e0272546&rft.epage=e0272546&rft.pages=e0272546-e0272546&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0272546&rft_dat=%3Cgale_plos_%3EA715079413%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2707187995&rft_id=info:pmid/36018862&rft_galeid=A715079413&rft_doaj_id=oai_doaj_org_article_052ab5cd4ebf4bc3abdb526ccd946d15&rfr_iscdi=true |