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Establishment of epidemic early warning index system and optimization of infectious disease model: Analysis on monitoring data of public health emergencies.
Xiong, Li; Hu, Peiyang; Wang, Houcai.
  • Xiong L; School of management, Shanghai University, Shanghai, China.
  • Hu P; School of management, Shanghai University, Shanghai, China.
  • Wang H; School of management, Shanghai University, Shanghai, China.
Int J Disaster Risk Reduct ; 65: 102547, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1385686
ABSTRACT
The ability to mitigate the damages caused by emergencies is an important symbol of the modernization of an emergency capability. When responding to emergencies, government agencies and decision makers need more information sources to estimate the possible evolution of the disaster in a more efficient manner. In this paper, an optimization model for predicting the dynamic evolution of COVID-19 is presented by combining the propagation algorithm of system dynamics with the warning indicators. By adding new parameters and taking the country as the research object, the epidemic situation in countries such as China, Japan, Korea, the United States and the United Kingdom was simulated and predicted, the impact of prevention and control measures such as effective contact coefficient on the epidemic situation was analyzed, and the effective contact coefficient of the country was analyzed. The paper strives to provide early warning of emergencies scientifically and effectively through the combination of these two technologies, and put forward feasible references for the implementation of various countermeasures. Judging from the conclusion, this study reaffirmed the importance of responding quickly to public health emergencies and formulating prevention and control policies to reduce population exposure and prevent the spread of the pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Int J Disaster Risk Reduct Year: 2021 Document Type: Article Affiliation country: J.ijdrr.2021.102547

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Int J Disaster Risk Reduct Year: 2021 Document Type: Article Affiliation country: J.ijdrr.2021.102547