{"title":"使用 SIR 和 AR 模型预测 COVID-19 病例:东京和全国范围内的应用","authors":"Tatsunori Seki, Tomoaki Sakurai, Satoshi Miyata, Keisuke Chujo, Toshiki Murata, Hiroyasu Inoue, Nobuyasu Ito","doi":"10.1007/s10015-024-00959-2","DOIUrl":null,"url":null,"abstract":"<div><p>With fast infectious diseases such as COVID-19, the SIR model may not represent the number of infections due to the occurrence of distribution shifts. In this study, we use simulations based on the SIR model to verify the prediction accuracy of new positive cases by considering distribution shifts. Instead of expressing the overall number of new positive cases in the SIR model, the number of new positive cases in a specific region is simulated, the expanded estimation ratio is expressed in the AR model, and these are multiplied to predict the overall number. In addition to the parameters used in the SIR model, we introduced parameters related to social variables. The parameters for the simulation were estimated daily from the data using approximate Bayesian computation (ABC). Using this method, the average absolute percent error in predicting the number of positive cases for the peak of the eighth wave (2022/12/22–12/28) for all of Japan was found to be 62.2% when using data up to two months before the peak and 6.2% when using data up to one month before the peak. Our simulations based on the SIR model reproduced the number of new positive cases across Japan and produced reasonable results when predicting the peak of the eighth wave.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"29 4","pages":"449 - 458"},"PeriodicalIF":0.8000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-024-00959-2.pdf","citationCount":"0","resultStr":"{\"title\":\"Prediction of COVID-19 cases using SIR and AR models: Tokyo-specific and nationwide application\",\"authors\":\"Tatsunori Seki, Tomoaki Sakurai, Satoshi Miyata, Keisuke Chujo, Toshiki Murata, Hiroyasu Inoue, Nobuyasu Ito\",\"doi\":\"10.1007/s10015-024-00959-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With fast infectious diseases such as COVID-19, the SIR model may not represent the number of infections due to the occurrence of distribution shifts. In this study, we use simulations based on the SIR model to verify the prediction accuracy of new positive cases by considering distribution shifts. Instead of expressing the overall number of new positive cases in the SIR model, the number of new positive cases in a specific region is simulated, the expanded estimation ratio is expressed in the AR model, and these are multiplied to predict the overall number. In addition to the parameters used in the SIR model, we introduced parameters related to social variables. The parameters for the simulation were estimated daily from the data using approximate Bayesian computation (ABC). Using this method, the average absolute percent error in predicting the number of positive cases for the peak of the eighth wave (2022/12/22–12/28) for all of Japan was found to be 62.2% when using data up to two months before the peak and 6.2% when using data up to one month before the peak. Our simulations based on the SIR model reproduced the number of new positive cases across Japan and produced reasonable results when predicting the peak of the eighth wave.</p></div>\",\"PeriodicalId\":46050,\"journal\":{\"name\":\"Artificial Life and Robotics\",\"volume\":\"29 4\",\"pages\":\"449 - 458\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10015-024-00959-2.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Life and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10015-024-00959-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-024-00959-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
摘要
对于 COVID-19 这样的快速传染病,SIR 模型可能无法代表感染病例的数量,因为会发生分布转移。在本研究中,我们使用基于 SIR 模型的模拟,通过考虑分布变化来验证新阳性病例的预测准确性。在 SIR 模型中,我们并不表示新发阳性病例的总体数量,而是模拟特定区域的新发阳性病例数量,在 AR 模型中表示扩大的估计比率,然后将两者相乘来预测总体数量。除了 SIR 模型中使用的参数外,我们还引入了与社会变量相关的参数。模拟的参数是每天使用近似贝叶斯计算法(ABC)从数据中估算出来的。通过这种方法,我们发现在预测日本全国第八波高峰期(2022/12/22-12/28)的阳性病例数时,如果使用高峰期前两个月的数据,平均绝对误差为 62.2%;如果使用高峰期前一个月的数据,平均绝对误差为 6.2%。我们基于 SIR 模型的模拟再现了日本全国新增阳性病例的数量,并在预测第八波高峰时得出了合理的结果。
Prediction of COVID-19 cases using SIR and AR models: Tokyo-specific and nationwide application
With fast infectious diseases such as COVID-19, the SIR model may not represent the number of infections due to the occurrence of distribution shifts. In this study, we use simulations based on the SIR model to verify the prediction accuracy of new positive cases by considering distribution shifts. Instead of expressing the overall number of new positive cases in the SIR model, the number of new positive cases in a specific region is simulated, the expanded estimation ratio is expressed in the AR model, and these are multiplied to predict the overall number. In addition to the parameters used in the SIR model, we introduced parameters related to social variables. The parameters for the simulation were estimated daily from the data using approximate Bayesian computation (ABC). Using this method, the average absolute percent error in predicting the number of positive cases for the peak of the eighth wave (2022/12/22–12/28) for all of Japan was found to be 62.2% when using data up to two months before the peak and 6.2% when using data up to one month before the peak. Our simulations based on the SIR model reproduced the number of new positive cases across Japan and produced reasonable results when predicting the peak of the eighth wave.