{"title":"从流行病剖面波中提取流行病学信息的新方法","authors":"Juan Campos , Maria C.A. Leite","doi":"10.1016/j.idm.2025.08.003","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we develop a novel mathematical framework based on the Kermack- McKendrick model to extract epidemiological parameters from real temporal profiles consisting of waves. The approach's key feature is the ability to obtain all model parameters from the geometry of the wave of interest.</div><div>We propose three new quantities to measure the negative impact of the epidemic wave on a specific population, called <em>Fraction of endemicity</em>, <em>Severity</em>, and <em>Asymmetry</em>. These three measures, along with a refined definition of the <em>basic reproduction number</em>, provide crucial epidemiological information.</div><div>We demonstrate analytically that there is an equivalence among these quantities, and such equivalence gives a way of obtaining all parameters in the model since the <em>Asymmetry</em> of a real epidemic wave is easily computed. This is the heart of the novel methodology we introduce. The framework is suitable for public health decision support, as its implementation does not rely on complex mathematical tools.</div><div>We present several case studies to illustrate the simplicity of the framework as well as the distinct aspects of its implementation. In all examples investigated, the numeric solution obtained with the parameterized model shows good agreement with the available data.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 87-106"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel approach to extract epidemiological information from waves in epidemic's profiles\",\"authors\":\"Juan Campos , Maria C.A. Leite\",\"doi\":\"10.1016/j.idm.2025.08.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, we develop a novel mathematical framework based on the Kermack- McKendrick model to extract epidemiological parameters from real temporal profiles consisting of waves. The approach's key feature is the ability to obtain all model parameters from the geometry of the wave of interest.</div><div>We propose three new quantities to measure the negative impact of the epidemic wave on a specific population, called <em>Fraction of endemicity</em>, <em>Severity</em>, and <em>Asymmetry</em>. These three measures, along with a refined definition of the <em>basic reproduction number</em>, provide crucial epidemiological information.</div><div>We demonstrate analytically that there is an equivalence among these quantities, and such equivalence gives a way of obtaining all parameters in the model since the <em>Asymmetry</em> of a real epidemic wave is easily computed. This is the heart of the novel methodology we introduce. The framework is suitable for public health decision support, as its implementation does not rely on complex mathematical tools.</div><div>We present several case studies to illustrate the simplicity of the framework as well as the distinct aspects of its implementation. In all examples investigated, the numeric solution obtained with the parameterized model shows good agreement with the available data.</div></div>\",\"PeriodicalId\":36831,\"journal\":{\"name\":\"Infectious Disease Modelling\",\"volume\":\"11 1\",\"pages\":\"Pages 87-106\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infectious Disease Modelling\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S246804272500079X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infectious Disease Modelling","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S246804272500079X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Novel approach to extract epidemiological information from waves in epidemic's profiles
In this paper, we develop a novel mathematical framework based on the Kermack- McKendrick model to extract epidemiological parameters from real temporal profiles consisting of waves. The approach's key feature is the ability to obtain all model parameters from the geometry of the wave of interest.
We propose three new quantities to measure the negative impact of the epidemic wave on a specific population, called Fraction of endemicity, Severity, and Asymmetry. These three measures, along with a refined definition of the basic reproduction number, provide crucial epidemiological information.
We demonstrate analytically that there is an equivalence among these quantities, and such equivalence gives a way of obtaining all parameters in the model since the Asymmetry of a real epidemic wave is easily computed. This is the heart of the novel methodology we introduce. The framework is suitable for public health decision support, as its implementation does not rely on complex mathematical tools.
We present several case studies to illustrate the simplicity of the framework as well as the distinct aspects of its implementation. In all examples investigated, the numeric solution obtained with the parameterized model shows good agreement with the available data.
期刊介绍:
Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.