{"title":"确定SIR流行病模型中未报告病例的数量。","authors":"A Ducrot, P Magal, T Nguyen, G F Webb","doi":"10.1093/imammb/dqz013","DOIUrl":null,"url":null,"abstract":"<p><p>An SIR epidemic model is analysed with respect to the identification of its parameters and initial values, based upon reported case data from public health sources. The objective of the analysis is to understand the relationship of unreported cases to reported cases. In many epidemic diseases the reported cases are a small fraction of the unreported cases. This fraction can be estimated by the identification of parameters for the model from reported case data. The analysis is applied to the Hong Kong seasonal influenza epidemic in New York City in 1968-1969.</p>","PeriodicalId":49863,"journal":{"name":"Mathematical Medicine and Biology-A Journal of the Ima","volume":"37 2","pages":"243-261"},"PeriodicalIF":0.8000,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/imammb/dqz013","citationCount":"23","resultStr":"{\"title\":\"Identifying the number of unreported cases in SIR epidemic models.\",\"authors\":\"A Ducrot, P Magal, T Nguyen, G F Webb\",\"doi\":\"10.1093/imammb/dqz013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>An SIR epidemic model is analysed with respect to the identification of its parameters and initial values, based upon reported case data from public health sources. The objective of the analysis is to understand the relationship of unreported cases to reported cases. In many epidemic diseases the reported cases are a small fraction of the unreported cases. This fraction can be estimated by the identification of parameters for the model from reported case data. The analysis is applied to the Hong Kong seasonal influenza epidemic in New York City in 1968-1969.</p>\",\"PeriodicalId\":49863,\"journal\":{\"name\":\"Mathematical Medicine and Biology-A Journal of the Ima\",\"volume\":\"37 2\",\"pages\":\"243-261\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2020-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1093/imammb/dqz013\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Medicine and Biology-A Journal of the Ima\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/imammb/dqz013\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Medicine and Biology-A Journal of the Ima","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/imammb/dqz013","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOLOGY","Score":null,"Total":0}
Identifying the number of unreported cases in SIR epidemic models.
An SIR epidemic model is analysed with respect to the identification of its parameters and initial values, based upon reported case data from public health sources. The objective of the analysis is to understand the relationship of unreported cases to reported cases. In many epidemic diseases the reported cases are a small fraction of the unreported cases. This fraction can be estimated by the identification of parameters for the model from reported case data. The analysis is applied to the Hong Kong seasonal influenza epidemic in New York City in 1968-1969.
期刊介绍：
Formerly the IMA Journal of Mathematics Applied in Medicine and Biology.
Mathematical Medicine and Biology publishes original articles with a significant mathematical content addressing topics in medicine and biology. Papers exploiting modern developments in applied mathematics are particularly welcome. The biomedical relevance of mathematical models should be demonstrated clearly and validation by comparison against experiment is strongly encouraged.
The journal welcomes contributions relevant to any area of the life sciences including:
-biomechanics-
biophysics-
cell biology-
developmental biology-
ecology and the environment-
epidemiology-
immunology-
infectious diseases-
neuroscience-
pharmacology-
physiology-
population biology
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