Loading…

Comparative and quantitative analysis of COVID-19 epidemic interventions in Chinese provinces

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 interve...

Full description

Saved in:
Bibliographic Details
Published in:Results in physics 2021-06, Vol.25, p.104305-104305, Article 104305
Main Authors: Liu, Huan, Rong, Zhiwei, Qi, Xinye, Fu, Jinming, Huang, Hao, Cao, Lei, Shan, Linghan, Zhao, Yashuang, Li, Kang, Hao, Yanhua, Jiao, Mingli, Wu, Qunhong, Zhang, Xue
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2211-3797
2211-3797
DOI:10.1016/j.rinp.2021.104305