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Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models.
Rui, Jia; Zheng, Jin-Xin; Chen, Jin; Wei, Hongjie; Yu, Shanshan; Zhao, Zeyu; Wang, Xin-Yi; Chen, Mu-Xin; Xia, Shang; Zhou, Ying; Chen, Tianmu; Zhou, Xiao-Nong.
Afiliação
  • Rui J; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
  • Zheng JX; Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
  • Chen J; National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China.
  • Wei H; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
  • Yu S; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
  • Zhao Z; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
  • Wang XY; National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China.
  • Chen MX; National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China.
  • Xia S; National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China.
  • Zhou Y; School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
  • Chen T; School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China. 14966898@qq.com.
  • Zhou XN; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China. 13698665@qq.com.
Infect Dis Poverty ; 11(1): 115, 2022 Nov 26.
Article em En | MEDLINE | ID: mdl-36435792
ABSTRACT

BACKGROUND:

There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory.

METHODS:

Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk.

RESULTS:

Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81-303.84) and the actual number of the spillovers (0.94-74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value.

CONCLUSIONS:

Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022

Texto completo: 1 Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022