具有潜在周期性行为的广义ODE易感-感染-易感区室模型

IF 8.8 3区 医学 Q1 Medicine
Scott Greenhalgh , Anna Dumas
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引用次数: 1

摘要

微分方程区隔模型是预测和分析疾病轨迹的重要工具。在这些模型中,那些只处理易感和传染性个体的模型特别有用,因为它们提供了解的封闭形式表达式,即logistic方程。然而,逻辑方程描述疾病轨迹的能力有限,因为它的解必须单调地收敛于无病平衡或地方病平衡,这取决于参数。不幸的是,许多疾病表现出周期性循环,因此,不会收敛到平衡状态。为了解决这一限制,我们开发了一种广义的易感-感染-易感区隔模型,该模型能够准确地结合感染分布的持续时间,并描述周期性和非周期性疾病轨迹。我们描述了我们的模型参数如何影响其行为,并应用该模型预测美国淋病发病率,使用赤池信息标准来告知其相对于传统SIS模型的优点。本文工作的意义在于提供了一种新的易感-感染-易感模型,该模型的解可以具有周期或非周期的封闭表达式,取决于参数化。因此,我们的工作为疾病建模者提供了一种直接的方法来调查许多疾病的潜在周期性行为,从而可能有助于正在进行的预防复发性爆发的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A generalized ODE susceptible-infectious-susceptible compartmental model with potentially periodic behavior

Differential equation compartmental models are crucial tools for forecasting and analyzing disease trajectories. Among these models, those dealing with only susceptible and infectious individuals are particularly useful as they offer closed-form expressions for solutions, namely the logistic equation. However, the logistic equation has limited ability to describe disease trajectories since its solutions must converge monotonically to either the disease-free or endemic equilibrium, depending on the parameters. Unfortunately, many diseases exhibit periodic cycles, and thus, do not converge to equilibria. To address this limitation, we developed a generalized susceptible-infectious-susceptible compartmental model capable of accurately incorporating the duration of infection distribution and describing both periodic and non-periodic disease trajectories. We characterized how our model's parameters influence its behavior and applied the model to predict gonorrhea incidence in the US, using Akaike Information Criteria to inform on its merit relative to the traditional SIS model. The significance of our work lies in providing a novel susceptible-infected-susceptible model whose solutions can have closed-form expressions that may be periodic or non-periodic depending on the parameterization. Our work thus provides disease modelers with a straightforward way to investigate the potential periodic behavior of many diseases and thereby may aid ongoing efforts to prevent recurrent outbreaks.

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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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