Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis.
Identifieur interne : 000158 ( Main/Exploration ); précédent : 000157; suivant : 000159Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis.
Auteurs : Zeye Liu [République populaire de Chine] ; Shuai Huang ; Wenlong Lu [République populaire de Chine] ; Zhanhao Su [République populaire de Chine] ; Xin Yin [République populaire de Chine] ; Huiying Liang ; Hao Zhang [République populaire de Chine]Source :
- Global health research and policy [ 2397-0642 ] ; 2020.
Abstract
Background
To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.
Methods
Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.
Results
The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month.
Conclusion
The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.
DOI: 10.1186/s41256-020-00145-4
PubMed: 32391439
PubMed Central: PMC7200323
Affiliations:
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type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Huang, Shuai" sort="Huang, Shuai" uniqKey="Huang S" first="Shuai" last="Huang">Shuai Huang</name><affiliation><nlm:affiliation>2Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou, 510623 Guangdong China.</nlm:affiliation><wicri:noCountry code="subField">510623 Guangdong China</wicri:noCountry></affiliation></author><author><name sortKey="Lu, Wenlong" sort="Lu, Wenlong" uniqKey="Lu W" first="Wenlong" last="Lu">Wenlong Lu</name><affiliation wicri:level="3"><nlm:affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular 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Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Huang, Shuai" sort="Huang, Shuai" uniqKey="Huang S" first="Shuai" last="Huang">Shuai Huang</name><affiliation><nlm:affiliation>2Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou, 510623 Guangdong China.</nlm:affiliation><wicri:noCountry code="subField">510623 Guangdong China</wicri:noCountry></affiliation></author><author><name sortKey="Lu, Wenlong" sort="Lu, Wenlong" uniqKey="Lu W" first="Wenlong" last="Lu">Wenlong Lu</name><affiliation wicri:level="3"><nlm:affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Su, Zhanhao" sort="Su, Zhanhao" uniqKey="Su Z" first="Zhanhao" last="Su">Zhanhao Su</name><affiliation wicri:level="3"><nlm:affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical 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China.</nlm:affiliation><wicri:noCountry code="subField">510623 Guangdong China</wicri:noCountry></affiliation></author><author><name sortKey="Zhang, Hao" sort="Zhang, Hao" uniqKey="Zhang H" first="Hao" last="Zhang">Hao Zhang</name><affiliation wicri:level="1"><nlm:affiliation>4Heart center and Shanghai Institute of Pediatric Congenital Heart Disease, Shanghai Children's Medical Center, National Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200127 China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>4Heart center and Shanghai Institute of Pediatric Congenital Heart Disease, Shanghai Children's Medical Center, National Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai</wicri:regionArea><wicri:noRegion>Shanghai</wicri:noRegion></affiliation></author></analytic><series><title level="j">Global health research and policy</title><idno type="eISSN">2397-0642</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>Background</b></p><p>To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.</p></div><div type="abstract" xml:lang="en"><p><b>Methods</b></p><p>Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.</p></div><div type="abstract" xml:lang="en"><p><b>Results</b></p><p>The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month.</p></div><div type="abstract" xml:lang="en"><p><b>Conclusion</b></p><p>The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.</p></div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32391439</PMID><DateRevised><Year>2020</Year><Month>05</Month><Day>15</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">2397-0642</ISSN><JournalIssue CitedMedium="Internet"><Volume>5</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Global health research and policy</Title><ISOAbbreviation>Glob Health Res Policy</ISOAbbreviation></Journal><ArticleTitle>Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis.</ArticleTitle><Pagination><MedlinePgn>20</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s41256-020-00145-4</ELocationID><Abstract><AbstractText Label="Background" NlmCategory="UNASSIGNED">To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.</AbstractText><AbstractText Label="Methods" NlmCategory="UNASSIGNED">Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.</AbstractText><AbstractText Label="Results" NlmCategory="UNASSIGNED">The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month.</AbstractText><AbstractText Label="Conclusion" NlmCategory="UNASSIGNED">The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.</AbstractText><CopyrightInformation>© The Author(s) 2020.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y" EqualContrib="Y"><LastName>Liu</LastName><ForeName>Zeye</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</Affiliation><Identifier Source="GRID">grid.506261.6</Identifier><Identifier Source="ISNI">0000 0001 0706 7839</Identifier></AffiliationInfo></Author><Author ValidYN="Y" EqualContrib="Y"><LastName>Huang</LastName><ForeName>Shuai</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>2Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou, 510623 Guangdong China.</Affiliation><Identifier Source="GRID">grid.413428.8</Identifier><Identifier Source="ISNI">0000 0004 1757 8466</Identifier></AffiliationInfo></Author><Author ValidYN="Y" EqualContrib="Y"><LastName>Lu</LastName><ForeName>Wenlong</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</Affiliation><Identifier Source="GRID">grid.506261.6</Identifier><Identifier Source="ISNI">0000 0001 0706 7839</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Su</LastName><ForeName>Zhanhao</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China.</Affiliation><Identifier Source="GRID">grid.506261.6</Identifier><Identifier Source="ISNI">0000 0001 0706 7839</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yin</LastName><ForeName>Xin</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>3School of Software & Microelectronics, Peking University, Beijing, 102600 China.</Affiliation><Identifier Source="GRID">grid.11135.37</Identifier><Identifier Source="ISNI">0000 0001 2256 9319</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liang</LastName><ForeName>Huiying</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>2Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou, 510623 Guangdong China.</Affiliation><Identifier Source="GRID">grid.413428.8</Identifier><Identifier Source="ISNI">0000 0004 1757 8466</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Hao</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>4Heart center and Shanghai Institute of Pediatric Congenital Heart Disease, Shanghai Children's Medical Center, National Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200127 China.</Affiliation><Identifier Source="GRID">grid.16821.3c</Identifier><Identifier Source="ISNI">0000 0004 0368 8293</Identifier></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Glob Health Res Policy</MedlineTA><NlmUniqueID>101705789</NlmUniqueID><ISSNLinking>2397-0642</ISSNLinking></MedlineJournalInfo><CoiStatement>Competing interestsThe authors declare that they have no competing interests.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>03</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>03</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>03</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>5</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>5</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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Liang</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Liu, Zeye" sort="Liu, Zeye" uniqKey="Liu Z" first="Zeye" last="Liu">Zeye Liu</name></noRegion><name sortKey="Lu, Wenlong" sort="Lu, Wenlong" uniqKey="Lu W" first="Wenlong" last="Lu">Wenlong Lu</name><name sortKey="Su, Zhanhao" sort="Su, Zhanhao" uniqKey="Su Z" first="Zhanhao" last="Su">Zhanhao Su</name><name sortKey="Yin, Xin" sort="Yin, Xin" uniqKey="Yin X" first="Xin" last="Yin">Xin Yin</name><name sortKey="Zhang, Hao" sort="Zhang, Hao" uniqKey="Zhang H" first="Hao" last="Zhang">Hao Zhang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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