[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].
Identifieur interne : 000051 ( an2020/Analysis ); précédent : 000050; suivant : 000052[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].
Auteurs : Y Y Wei [République populaire de Chine] ; Z Z Lu [République populaire de Chine] ; Z C Du [République populaire de Chine] ; Z J Zhang [République populaire de Chine] ; Y. Zhao [République populaire de Chine] ; S P Shen [République populaire de Chine] ; B. Wang [République populaire de Chine] ; Y T Hao [République populaire de Chine] ; F. Chen [République populaire de Chine]Source :
- Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi [ 0254-6450 ] ; 2020.
Abstract
Objectives: Fitting and forecasting the trend of COVID-19 epidemics. Methods: Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+ CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results: According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+ CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions: The proposed SEIR(+ CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.
DOI: 10.3760/cma.j.cn112338-20200216-00106
PubMed: 32113198
Affiliations:
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Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32113198</idno><idno type="pmid">32113198</idno><idno type="doi">10.3760/cma.j.cn112338-20200216-00106</idno><idno type="wicri:Area/PubMed/Corpus">000389</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000389</idno><idno type="wicri:Area/PubMed/Curation">000389</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000389</idno><idno type="wicri:Area/PubMed/Checkpoint">000045</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000045</idno><idno type="wicri:Area/Ncbi/Merge">000A23</idno><idno type="wicri:Area/Ncbi/Curation">000A23</idno><idno type="wicri:Area/Ncbi/Checkpoint">000A23</idno><idno type="wicri:doubleKey">0254-6450:2020:Wei Y:fitting:and:forecasting</idno><idno type="wicri:Area/Main/Merge">000053</idno><idno type="wicri:Area/Main/Curation">000051</idno><idno type="wicri:Area/Main/Exploration">000051</idno><idno type="wicri:Area/an2020/Extraction">000051</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].</title><author><name sortKey="Wei, Y Y" sort="Wei, Y Y" uniqKey="Wei Y" first="Y Y" last="Wei">Y Y Wei</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author><author><name sortKey="Lu, Z Z" sort="Lu, Z Z" uniqKey="Lu Z" first="Z Z" last="Lu">Z Z Lu</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author><author><name sortKey="Du, Z C" sort="Du, Z C" uniqKey="Du Z" first="Z C" last="Du">Z C Du</name><affiliation wicri:level="1"><nlm:affiliation>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Zhang, Z J" sort="Zhang, Z J" uniqKey="Zhang Z" first="Z J" last="Zhang">Z J Zhang</name><affiliation wicri:level="1"><nlm:affiliation>Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032</wicri:regionArea><wicri:noRegion>Shanghai 200032</wicri:noRegion></affiliation></author><author><name sortKey="Zhao, Y" sort="Zhao, Y" uniqKey="Zhao Y" first="Y" last="Zhao">Y. Zhao</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author><author><name sortKey="Shen, S P" sort="Shen, S P" uniqKey="Shen S" first="S P" last="Shen">S P Shen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author><author><name sortKey="Wang, B" sort="Wang, B" uniqKey="Wang B" first="B" last="Wang">B. Wang</name><affiliation wicri:level="1"><nlm:affiliation>Meinian Institute of Health, Beijing 100191, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Meinian Institute of Health, Beijing 100191</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Hao, Y T" sort="Hao, Y T" uniqKey="Hao Y" first="Y T" last="Hao">Y T Hao</name><affiliation wicri:level="1"><nlm:affiliation>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Chen, F" sort="Chen, F" uniqKey="Chen F" first="F" last="Chen">F. Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></affiliation></author></analytic><series><title level="j">Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi</title><idno type="ISSN">0254-6450</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"><b>Objectives:</b> Fitting and forecasting the trend of COVID-19 epidemics. <b>Methods:</b> Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+ CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. <b>Results:</b> According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+ CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. <b>Conclusions:</b> The proposed SEIR(+ CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Jiangmen</li><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wei, Y Y" sort="Wei, Y Y" uniqKey="Wei Y" first="Y Y" last="Wei">Y Y Wei</name></noRegion><name sortKey="Chen, F" sort="Chen, F" uniqKey="Chen F" first="F" last="Chen">F. Chen</name><name sortKey="Du, Z C" sort="Du, Z C" uniqKey="Du Z" first="Z C" last="Du">Z C Du</name><name sortKey="Hao, Y T" sort="Hao, Y T" uniqKey="Hao Y" first="Y T" last="Hao">Y T Hao</name><name sortKey="Lu, Z Z" sort="Lu, Z Z" uniqKey="Lu Z" first="Z Z" last="Lu">Z Z Lu</name><name sortKey="Shen, S P" sort="Shen, S P" uniqKey="Shen S" first="S P" last="Shen">S P Shen</name><name sortKey="Wang, B" sort="Wang, B" uniqKey="Wang B" first="B" last="Wang">B. Wang</name><name sortKey="Zhang, Z J" sort="Zhang, Z J" uniqKey="Zhang Z" first="Z J" last="Zhang">Z J Zhang</name><name sortKey="Zhao, Y" sort="Zhao, Y" uniqKey="Zhao Y" first="Y" last="Zhao">Y. Zhao</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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