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广义多菌株SIS模型的动力学与持续性

Dynamics and Persistence of a Generalized Multi-strain SIS Model.

作者信息

Greenhalgh Scott, Henriquez Tabitha, Frutschy Michael, Leonard Rebecah

机构信息

Department of Mathematics, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA.

Data Science Program, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA.

出版信息

Bull Math Biol. 2025 Sep 10;87(10):147. doi: 10.1007/s11538-025-01516-z.

Abstract

Autonomous differential equation compartmental models hold broad utility in epidemiology and public health. However, these models typically cannot account explicitly for myriad factors that affect the trajectory of infectious diseases, with seasonal variations in host behavior and environmental conditions as noteworthy examples. Fortunately, using non-autonomous differential equation compartmental models can mitigate some of these deficiencies, as the inclusion of time-varying parameters can account for temporally varying factors. The inclusion of these temporally varying factors does come at a cost though, as many analysis techniques, such as the use of Poincaré maps and Floquet theory, on non-autonomous differential equation compartmental models are typically only tractable numerically. Here, we illustrate a rare -strain generalized Susceptible-Infectious-Susceptible (SIS) compartmental model, with a general time-varying recovery rate, which features Floquet exponents that are algebraic expressions. We completely characterize the persistence and stability properties of our -strain generalized SIS model for . We also derive a closed-form solution in terms of elementary functions for the single-strain SIS model, which is capable of incorporating almost any infectious period distribution. Finally, to demonstrate the applicability of our work, we apply it to recent syphilis incidence data from the United States, utilizing Akaike Information Criteria and Forecast Skill Scores to inform on the model's goodness of fit relative to complexity and the model's capacity to predict future trends.

摘要

自治微分方程 compartmental 模型在流行病学和公共卫生领域具有广泛的用途。然而,这些模型通常无法明确考虑影响传染病传播轨迹的众多因素,宿主行为和环境条件的季节性变化就是值得注意的例子。幸运的是,使用非自治微分方程 compartmental 模型可以减轻其中一些不足,因为纳入随时间变化的参数可以考虑随时间变化的因素。不过,纳入这些随时间变化的因素是有代价的,因为许多分析技术,例如在非自治微分方程 compartmental 模型上使用庞加莱映射和弗洛凯理论,通常只能通过数值方法来处理。在此,我们展示了一个罕见的多菌株广义易感 - 感染 - 易感(SIS)compartmental 模型,其具有一般的随时间变化的恢复率,该模型的弗洛凯指数是代数表达式。我们完全刻画了我们的多菌株广义 SIS 模型对于特定情况的持久性和稳定性特性。我们还针对单菌株 SIS 模型推导出了一个用初等函数表示的封闭形式解,该解能够纳入几乎任何感染期分布。最后,为了证明我们工作的适用性,我们将其应用于美国最近的梅毒发病率数据,利用赤池信息准则和预测技能分数来评估模型相对于复杂性的拟合优度以及模型预测未来趋势的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be48/12423140/755b257483ff/11538_2025_1516_Fig1_HTML.jpg

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