具有季节性发育期和固有潜伏期的反应-平流-扩散登革热模型的传播动力学

IF 2.2 4区 数学 Q2 BIOLOGY
Yijie Zha, Weihua Jiang
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引用次数: 0

摘要

在本文中,我们提出了一个具有季节性发展持续时间和内在潜伏期的反应-平流-扩散登革热模型。首先,我们建立了模型的拟合性。其次,我们定义了该模型的基本繁殖数\( {\Re _{0} \),并证明\( {\Re _0} \)是一个阈值参数:如果\( {\Re _0} <1 \),则无病周期解具有全局吸引力;如果\( {\Re _0}>1 \),则系统具有均匀持久性。第三,我们研究了当空间环境均匀且忽略蚊子平流时,正稳态的全局吸引力。我们以中国广东省登革热传播为例,探讨了模型参数对 \( \Re _{0}\)的影响。我们的研究结果表明,忽略季节性可能会低估 \( \Re _{0}\)。此外,传播的空间异质性可能会增加疾病传播的风险,而增加季节性发育持续时间、内在潜伏期和平流率都可以降低疾病传播的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transmission dynamics of a reaction–advection–diffusion dengue fever model with seasonal developmental durations and intrinsic incubation periods

Transmission dynamics of a reaction–advection–diffusion dengue fever model with seasonal developmental durations and intrinsic incubation periods

In this paper, we propose a reaction–advection–diffusion dengue fever model with seasonal developmental durations and intrinsic incubation periods. Firstly, we establish the well-posedness of the model. Secondly, we define the basic reproduction number \( \Re _{0} \) for this model and show that \( {\Re _0} \) is a threshold parameter: if \( {\Re _0} <1 \), then the disease-free periodic solution is globally attractive; if \( {\Re _0}>1 \), the system is uniformly persistent. Thirdly, we study the global attractivity of the positive steady state when the spatial environment is homogeneous and the advection of mosquitoes is ignored. As an example, we use the model to investigate the dengue fever transmission case in Guangdong Province, China, and explore the impact of model parameters on \( \Re _{0}\). Our findings indicate that ignoring seasonality may underestimate \(\Re _0\). Additionally, the spatial heterogeneity of transmission may increase the risk of disease transmission, while the increase of seasonal developmental durations, intrinsic incubation periods and advection rates can all reduce the risk of disease transmission.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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