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Real-time assessment of COVID-19 epidemic in Guangdong Province, China using mathematical models.
Zeng, Zhiqi; Qu, Wei; Liu, Ruibin; Guan, Wenda; Liang, Jingyi; Lin, Zhijie; Lau, Eric H Y; Hon, Chitin; Yang, Zifeng; He, Jianxing.
Afiliação
  • Zeng Z; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Qu W; Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
  • Liu R; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Guan W; College of Sciences, China Jiliang University, Hangzhou, China.
  • Liang J; Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China.
  • Lin Z; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Lau EHY; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Hon C; Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China.
  • Yang Z; School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • He J; Laboratory of Data Discovery for Health, Tai Po, Hong Kong, China.
J Thorac Dis ; 15(3): 1517-1522, 2023 Mar 31.
Article em En | MEDLINE | ID: mdl-37065584
China government has relaxed the response measures of COVID-19 in early December 2022. In this report, we assessed the number of infections, the number of severe cases based on the current epidemic trend (October 22, 2022 to November 30, 2022) using a transmission dynamics model, called modified susceptible-exposed-infectious-removed (SEIR) to provide valuable information to ensure the medical operation of the healthcare system under the new situation. Our model showed that the present outbreak in Guangdong Province peaked during December 21, 2022 to December 25, 2022 with about 14.98 million new infections (95% CI: 14.23-15.73 million). The cumulative number of infections will reach about 70% of the province's population from December 24, 2022 to December 26, 2022. The number of existing severe cases is expected to peak during January 1, 2023 to January 5, 2023 with a peak number of approximately 101.45 thousand (95% CI: 96.38-106.52 thousand). In addition, the epidemic in Guangzhou which is the capital city of Guangdong Province is expected to have peaked around December 22, 2022 to December 23, 2022 with the number of new infections at the peak being about 2.45 million (95% CI: 2.33-2.57 million). The cumulative number of infected people will reach about 70% of the city's population from December 24, 2022 to December 25, 2022 and the number of existing severe cases is expected to peak around January 4, 2023 to January 6, 2023 with the number of existing severe cases at the peak being about 6.32 thousand (95% CI: 6.00-6.64 thousand). Predicted results enable the government to prepare medically and plan for potential risks in advance.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article