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A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic.
Deng, Qing; Xiao, Xingyu; Zhu, Lin; Cao, Xue; Liu, Kai; Zhang, Hui; Huang, Lida; Yu, Feng; Jiang, Huiling; Liu, Yi.
  • Deng Q; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Xiao X; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Zhu L; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Cao X; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Liu K; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Zhang H; Deparment of Engineering Physics, Tsinghua University, Beijing, China.
  • Huang L; Deparment of Engineering Physics, Tsinghua University, Beijing, China.
  • Yu F; School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China.
  • Jiang H; School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China.
  • Liu Y; School of Public Security and Traffic Management, People's Public Security University of China, Beijing, China.
Risk Anal ; 2023 Jan 08.
Article in English | MEDLINE | ID: covidwho-2193200
ABSTRACT
COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Variants Language: English Year: 2023 Document Type: Article Affiliation country: Risa.14087

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Variants Language: English Year: 2023 Document Type: Article Affiliation country: Risa.14087