从流行病剖面波中提取流行病学信息的新方法

IF 2.5 3区 医学 Q1 Medicine
Juan Campos , Maria C.A. Leite
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引用次数: 0

摘要

在本文中,我们基于Kermack- McKendrick模型开发了一个新的数学框架,从由波浪组成的真实时间剖面中提取流行病学参数。该方法的主要特点是能够从感兴趣的波的几何形状中获得所有模型参数。我们提出了三个新的量来衡量流行病波对特定人群的负面影响,称为地方性分数,严重性和不对称性。这三项措施以及对基本繁殖数的精确定义提供了重要的流行病学信息。我们解析地证明了这些量之间是等价的,并且这种等价提供了一种获得模型中所有参数的方法,因为实际流行波的不对称性很容易计算。这是我们介绍的新方法的核心。该框架适用于公共卫生决策支持,因为其实施不依赖于复杂的数学工具。我们提供了几个案例研究来说明框架的简单性及其实现的不同方面。在所研究的所有实例中,参数化模型得到的数值解与现有数据吻合良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
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.
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