{"title":"一个简单的SIDTR流行模式,使印度无结核病并停止传播","authors":"S. Priya, K. Ganesan","doi":"10.28919/cmbn/8001","DOIUrl":null,"url":null,"abstract":". In this paper, we construct a SIDTR model. We develop a system of differential equations for SIDTR (Suspected, Infected, Diagnosed, Treatment and Recovered) model and analyze the outbreak of Tuberculosis (TB) infection and its effect on Indian population. We established theorems on stability analysis conditions for disease free equilibrium and endemic equilibrium. The basic reproduction number R 0 was determined by using the next generation matrix. We attempt to fit our proposed mathematical model by using real world data which was taken from WHO. We expect that this study will be effective on controlling Tuberculosis (TB) spread and also we predicted the future TB infection in India.","PeriodicalId":44079,"journal":{"name":"Communications in Mathematical Biology and Neuroscience","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A simple SIDTR endemic model to make tuberculosis free India and stop spreading\",\"authors\":\"S. Priya, K. Ganesan\",\"doi\":\"10.28919/cmbn/8001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". In this paper, we construct a SIDTR model. We develop a system of differential equations for SIDTR (Suspected, Infected, Diagnosed, Treatment and Recovered) model and analyze the outbreak of Tuberculosis (TB) infection and its effect on Indian population. We established theorems on stability analysis conditions for disease free equilibrium and endemic equilibrium. The basic reproduction number R 0 was determined by using the next generation matrix. We attempt to fit our proposed mathematical model by using real world data which was taken from WHO. We expect that this study will be effective on controlling Tuberculosis (TB) spread and also we predicted the future TB infection in India.\",\"PeriodicalId\":44079,\"journal\":{\"name\":\"Communications in Mathematical Biology and Neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications in Mathematical Biology and Neuroscience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.28919/cmbn/8001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Mathematical Biology and Neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28919/cmbn/8001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A simple SIDTR endemic model to make tuberculosis free India and stop spreading
. In this paper, we construct a SIDTR model. We develop a system of differential equations for SIDTR (Suspected, Infected, Diagnosed, Treatment and Recovered) model and analyze the outbreak of Tuberculosis (TB) infection and its effect on Indian population. We established theorems on stability analysis conditions for disease free equilibrium and endemic equilibrium. The basic reproduction number R 0 was determined by using the next generation matrix. We attempt to fit our proposed mathematical model by using real world data which was taken from WHO. We expect that this study will be effective on controlling Tuberculosis (TB) spread and also we predicted the future TB infection in India.
期刊介绍:
Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.