WANG Ying,HU Jiancheng.Prediction and Analysis of COVID-19 Using PSO Algorithm for Estimating SEIRD Model Parameters[J].Journal of Chengdu University of Information Technology,2021,36(04):368-373.[doi:10.16836/j.cnki.jcuit.2021.04.003]
粒子群算法估计SEIRD模型参数的新冠肺炎疫情预测分析
- Title:
- Prediction and Analysis of COVID-19 Using PSO Algorithm for Estimating SEIRD Model Parameters
- 文章编号:
- 2096-1618(2021)04-0368-06
- Keywords:
- COVID-19; SEIRDmodel; PSO; SEQRDmodel; predict
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 通过建立SEIRD模型模拟新型冠状病毒在人群中的传播机制。选取湖北省2020年1月23日到2月16日疫情数据,建立SEIRD模型,根据SEIRD模型参数,构建优化模型,应用粒子群算法对参数进行估计,分析和预测湖北省新型冠状病毒性肺炎疫情拐点。考虑到政府采取的措施,用修正的SEQRD模型,重新对参数进行估计,模型显示在政府的干预下,疫情的拐点提前了,感染人数减少。
- Abstract:
- SEIRD model was used to simulate the spread of COVID-19 in the crowd.Based on the epidemic data of Hubei Province from January 23 to February 16,2020,SEIRD model was established. According to the parameters of SEIRD model, the optimization model was constructed, and the parameters were estimated by PSO algorithm to analyze and predict the inflection point of COVID-19 in Hubei Province.Considering the measures taken by the government, the parameters are re estimated by using the modified SEQRD model. The model shows that the inflection point of the epidemic is advanced and the number of infected people is reduced under the intervention of the government.
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备注/Memo
收稿日期:2021-05-21
基金项目:四川省科技厅科技计划资助项目(2019YFS0143)