Implementation of Clinical Diagnostic Criteria and Universal Symptom Survey Contributed to Lower Magnitude and Faster Resolution of the COVID-19 Epidemic in Wuhan.

COVID-19 Clinical diagnostic criteria Evaluation of the intervention effect Extended SEIR+Q dynamics model Universal symptom survey

Journal

Engineering (Beijing, China)
ISSN: 2095-8099
Titre abrégé: Engineering (Beijing)
Pays: China
ID NLM: 101673395

Informations de publication

Date de publication:
Oct 2020
Historique:
received: 01 04 2020
revised: 11 04 2020
accepted: 23 04 2020
pubmed: 10 5 2020
medline: 10 5 2020
entrez: 9 5 2020
Statut: ppublish

Résumé

The majority of cases infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China centered in the city of Wuhan. Despite a rapid increase in the number of cases and deaths due to the coronavirus disease 2019 (COVID-19), the epidemic was stemmed via a combination of epidemic mitigation and control measures. This study evaluates how the implementation of clinical diagnostics and universal symptom surveys contributed to epidemic control in Wuhan. We extended the susceptibles-exposed-infectious-removed (SEIR) transmission dynamics model by considering three quarantined compartments (SEIR

Identifiants

pubmed: 32382449
doi: 10.1016/j.eng.2020.04.008
pii: S2095-8099(20)30107-7
pmc: PMC7204764
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1141-1146

Informations de copyright

© 2020 THE AUTHORS.

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Auteurs

Yongyue Wei (Y)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Liangmin Wei (L)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Yue Jiang (Y)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Sipeng Shen (S)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Yang Zhao (Y)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Yuantao Hao (Y)

Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.

Zhicheng Du (Z)

Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.

Jinling Tang (J)

Guangzhou Women and Children's Medical Center, Guangzhou 510623, China.

Zhijie Zhang (Z)

Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.

Qingwu Jiang (Q)

Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.

Liming Li (L)

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.

Feng Chen (F)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Hongbing Shen (H)

Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Classifications MeSH