tetano
Editor, Senior Moderator
Results Phys
. 2021 May 13;104305.
doi: 10.1016/j.rinp.2021.104305. Online ahead of print.
Comparative and quantitative analysis of COVID-19 epidemic interventions in Chinese provinces
Huan Liu[SUP] 1 2 [/SUP], Howie Rong[SUP] 3 [/SUP], Xinye Qi[SUP] 1 2 [/SUP], Jinming Fu[SUP] 4 [/SUP], Hao Huang[SUP] 4 [/SUP], Lei Cao[SUP] 3 [/SUP], Linghan Shan[SUP] 1 2 [/SUP], Yashuang Zhao[SUP] 5 [/SUP], Kang Li[SUP] 3 [/SUP], Yanhua Hao[SUP] 1 2 [/SUP], Mingli Jiao[SUP] 1 2 [/SUP], Qunhong Wu[SUP] 1 2 [/SUP], Xue Zhang[SUP] 4 [/SUP]
Affiliations
Abstract
A mathematical model was developed to evaluate and compare the effects and intensity of the Coronavirus disease 2019 prevention and control measures in Chinese provinces. The time course of the disease with government intervention was described using a dynamic model. The estimated government intervention parameters and area difference between with and without intervention were considered as the intervention intensity and effect, respectively. The model of the disease time course without government intervention predicted that by April 30, 2020, about 3.08% of the population would have been diagnosed with Coronavirus disease 2019 in China. Guangdong Province averted the most cases. Comprehensive intervention measures, in which social distancing measures may have played a greater role than isolation measures, resulted in reduced infection cases. Shanghai had the highest intervention intensity. In the context of the global Coronavirus disease 2019 pandemic, the prevention and control experience of some key areas in China (such as Shanghai and Guangdong) can provide references for outbreak control in many countries. Abbreviations COVID-19: Coronavirus disease 2019; WHO: World Health Organization; R0: basic reproduction number; CDC: Chinese Center for Disease Control and Prevention; Rt: effective reproduction number; QR: healthy quick response; SARS: Severe Acute Respiratory Syndrome.
Keywords: Chinese provinces; Coronavirus disease 2019; Net-SEAIHRQ model; intervention effect intensity.
. 2021 May 13;104305.
doi: 10.1016/j.rinp.2021.104305. Online ahead of print.
Comparative and quantitative analysis of COVID-19 epidemic interventions in Chinese provinces
Huan Liu[SUP] 1 2 [/SUP], Howie Rong[SUP] 3 [/SUP], Xinye Qi[SUP] 1 2 [/SUP], Jinming Fu[SUP] 4 [/SUP], Hao Huang[SUP] 4 [/SUP], Lei Cao[SUP] 3 [/SUP], Linghan Shan[SUP] 1 2 [/SUP], Yashuang Zhao[SUP] 5 [/SUP], Kang Li[SUP] 3 [/SUP], Yanhua Hao[SUP] 1 2 [/SUP], Mingli Jiao[SUP] 1 2 [/SUP], Qunhong Wu[SUP] 1 2 [/SUP], Xue Zhang[SUP] 4 [/SUP]
Affiliations
- PMID: 34002128
- PMCID: PMC8117536
- DOI: 10.1016/j.rinp.2021.104305
Abstract
A mathematical model was developed to evaluate and compare the effects and intensity of the Coronavirus disease 2019 prevention and control measures in Chinese provinces. The time course of the disease with government intervention was described using a dynamic model. The estimated government intervention parameters and area difference between with and without intervention were considered as the intervention intensity and effect, respectively. The model of the disease time course without government intervention predicted that by April 30, 2020, about 3.08% of the population would have been diagnosed with Coronavirus disease 2019 in China. Guangdong Province averted the most cases. Comprehensive intervention measures, in which social distancing measures may have played a greater role than isolation measures, resulted in reduced infection cases. Shanghai had the highest intervention intensity. In the context of the global Coronavirus disease 2019 pandemic, the prevention and control experience of some key areas in China (such as Shanghai and Guangdong) can provide references for outbreak control in many countries. Abbreviations COVID-19: Coronavirus disease 2019; WHO: World Health Organization; R0: basic reproduction number; CDC: Chinese Center for Disease Control and Prevention; Rt: effective reproduction number; QR: healthy quick response; SARS: Severe Acute Respiratory Syndrome.
Keywords: Chinese provinces; Coronavirus disease 2019; Net-SEAIHRQ model; intervention effect intensity.