{"title":"基于LSTM和粒子群优化的COVID-19疫苗接种人数预测及疫苗分配策略","authors":"Yiqiao Zhang, Ping Cui, Guijin Xie","doi":"10.1109/UV56588.2022.10185507","DOIUrl":null,"url":null,"abstract":"This paper uses the LSTM network to predict the number of vaccinations in China from December 2022 to February 2023. In addition, according to the number of residents in different regions, the number of medical staff and other factors, the vaccine allocation optimization model is built. The model is solved by particle swarm optimization. The distribution strategy is applied to the analog data of Gongshu District of Hangzhou City and Daoli District of Harbin City. Finally, we give some implementable suggestions for the vaccination.","PeriodicalId":211011,"journal":{"name":"2022 6th International Conference on Universal Village (UV)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predict The Number of Vaccinated People and Formulate Vaccine Distribution Strategy of COVID-19 Based on LSTM and Particle Swarm optimization\",\"authors\":\"Yiqiao Zhang, Ping Cui, Guijin Xie\",\"doi\":\"10.1109/UV56588.2022.10185507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper uses the LSTM network to predict the number of vaccinations in China from December 2022 to February 2023. In addition, according to the number of residents in different regions, the number of medical staff and other factors, the vaccine allocation optimization model is built. The model is solved by particle swarm optimization. The distribution strategy is applied to the analog data of Gongshu District of Hangzhou City and Daoli District of Harbin City. Finally, we give some implementable suggestions for the vaccination.\",\"PeriodicalId\":211011,\"journal\":{\"name\":\"2022 6th International Conference on Universal Village (UV)\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 6th International Conference on Universal Village (UV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UV56588.2022.10185507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th International Conference on Universal Village (UV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UV56588.2022.10185507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predict The Number of Vaccinated People and Formulate Vaccine Distribution Strategy of COVID-19 Based on LSTM and Particle Swarm optimization
This paper uses the LSTM network to predict the number of vaccinations in China from December 2022 to February 2023. In addition, according to the number of residents in different regions, the number of medical staff and other factors, the vaccine allocation optimization model is built. The model is solved by particle swarm optimization. The distribution strategy is applied to the analog data of Gongshu District of Hangzhou City and Daoli District of Harbin City. Finally, we give some implementable suggestions for the vaccination.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
