随机模型在长期流行病预测中的应用

Q3 Engineering

Journal of Automation and Information Sciences Pub Date : 2021-05-01 DOI:10.34229/1028-0979-2021-3-4

P. Knopov, O. Bogdanov

{"title":"随机模型在长期流行病预测中的应用","authors":"P. Knopov, O. Bogdanov","doi":"10.34229/1028-0979-2021-3-4","DOIUrl":null,"url":null,"abstract":"In this paper we consider a stochastic discrete-time epidemic model, with the infectivity depending on the age of infection and existing formula for the maximum likelihood estimation of the parameter responsible for the rate of the infection spread. In order to utilize the real number of infection cases statistics, a detection rate parameter is introduced. A program for automatic parameter estimation using past data with future epidemic simulation is developed. We present the comparison between the simulation of COVID-19 cases in Kyiv and real data using manual and automatic parameter estimation. We consider the possibility of the epidemic partition into several intervals with different parameters in order to simulate lengthy epidemics with significant changes in dynamics. We present the comparison between different numbers of partitions for long-term COVID-19 simulation in Kyiv (Ukraine) and Czech Republic, which have different dynamics of the epidemic development.","PeriodicalId":54874,"journal":{"name":"Journal of Automation and Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"APPLICATION OF STOCHASTIC MODEL FOR LENGTHY EPIDEMIC FORECASTING\",\"authors\":\"P. Knopov, O. Bogdanov\",\"doi\":\"10.34229/1028-0979-2021-3-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we consider a stochastic discrete-time epidemic model, with the infectivity depending on the age of infection and existing formula for the maximum likelihood estimation of the parameter responsible for the rate of the infection spread. In order to utilize the real number of infection cases statistics, a detection rate parameter is introduced. A program for automatic parameter estimation using past data with future epidemic simulation is developed. We present the comparison between the simulation of COVID-19 cases in Kyiv and real data using manual and automatic parameter estimation. We consider the possibility of the epidemic partition into several intervals with different parameters in order to simulate lengthy epidemics with significant changes in dynamics. We present the comparison between different numbers of partitions for long-term COVID-19 simulation in Kyiv (Ukraine) and Czech Republic, which have different dynamics of the epidemic development.\",\"PeriodicalId\":54874,\"journal\":{\"name\":\"Journal of Automation and Information Sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Automation and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34229/1028-0979-2021-3-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Automation and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34229/1028-0979-2021-3-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}

引用次数: 0

摘要

在本文中，我们考虑了一个随机离散时间流行病模型，其传染性取决于感染年龄，以及影响感染传播率的参数的最大似然估计公式。为了利用感染病例的真实数量统计，引入了检测率参数。开发了一个利用过去数据和未来流行病模拟进行参数自动估计的程序。我们介绍了基辅新冠肺炎病例的模拟与使用手动和自动参数估计的真实数据之间的比较。我们考虑了将流行病划分为具有不同参数的几个区间的可能性，以便模拟具有显著动态变化的长期流行病。我们介绍了基辅（乌克兰）和捷克共和国新冠肺炎长期模拟的不同分区数量之间的比较，这两个国家的疫情发展动态不同。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

APPLICATION OF STOCHASTIC MODEL FOR LENGTHY EPIDEMIC FORECASTING

In this paper we consider a stochastic discrete-time epidemic model, with the infectivity depending on the age of infection and existing formula for the maximum likelihood estimation of the parameter responsible for the rate of the infection spread. In order to utilize the real number of infection cases statistics, a detection rate parameter is introduced. A program for automatic parameter estimation using past data with future epidemic simulation is developed. We present the comparison between the simulation of COVID-19 cases in Kyiv and real data using manual and automatic parameter estimation. We consider the possibility of the epidemic partition into several intervals with different parameters in order to simulate lengthy epidemics with significant changes in dynamics. We present the comparison between different numbers of partitions for long-term COVID-19 simulation in Kyiv (Ukraine) and Czech Republic, which have different dynamics of the epidemic development.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Automation and Information Sciences AUTOMATION & CONTROL SYSTEMS-

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： This journal contains translations of papers from the Russian-language bimonthly "Mezhdunarodnyi nauchno-tekhnicheskiy zhurnal "Problemy upravleniya i informatiki". Subjects covered include information sciences such as pattern recognition, forecasting, identification and evaluation of complex systems, information security, fault diagnosis and reliability. In addition, the journal also deals with such automation subjects as adaptive, stochastic and optimal control, control and identification under uncertainty, robotics, and applications of user-friendly computers in management of economic, industrial, biological, and medical systems. The Journal of Automation and Information Sciences will appeal to professionals in control systems, communications, computers, engineering in biology and medicine, instrumentation and measurement, and those interested in the social implications of technology.