Asymptotic behavior of the basic reproduction number for periodic nonlocal dispersal operators and applications.

IF 2.3 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2025-02-03 DOI:10.1007/s00285-025-02192-2
Yan-Xia Feng, Wan-Tong Li, Yuan Lou, Fei-Ying Yang
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Abstract

This paper is concerned with asymptotic behavior of the basic reproduction number defined by next generation nonlocal (convolution) dispersal operators in a time-periodic environment and applications. First we investigate the influence of the frequency and dispersal rate on the basic reproduction number, and we obtain that the basic reproduction number is monotone on the frequency. In the nonautonomous situation, the basic reproduction number is not a monotone function of dispersal rate in general. We derive the monotonicity for large frequency or dispersal rate. Then we apply the obtained results to a time-periodic SIS epidemic model and establish the existence and asymptotic profiles of the endemic periodic solution. Since solution maps of nonlocal system lack compactness, the standard uniform persistence theory and topological degree theory are unapplicable to obtain the existence of the endemic periodic solution. To overcome this difficulty, we apply the asymptotic fixed point theorem with the help of the Kuratowski measure of noncompactness.

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周期非局部扩散算子基本再现数的渐近性质及其应用。
研究了由新一代非局部(卷积)扩散算子定义的基本再生数在时间周期环境下的渐近行为及其应用。首先研究了频率和扩散速率对基本再生数的影响,得到了基本再生数对频率的单调性。在非自治情况下,基本繁殖数一般不是扩散率的单调函数。我们推导了大频率和大分散率下的单调性。然后将所得结果应用于一个时间周期SIS流行病模型,建立了流行病周期解的存在性和渐近曲线。由于非局部系统的解映射缺乏紧性,标准一致持续理论和拓扑度理论不能用于得到局部周期解的存在性。为了克服这一困难,我们借助于非紧性的库拉托夫斯基测度,应用了渐近不动点定理。
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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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