SIRS Epidemic Models with Delays, Partial and Temporary Immunity and Vaccination

被引:1
作者
Chen-Charpentier, Benito [1 ]
机构
[1] Univ Texas Arlington, Dept Math, Arlington, TX 76019 USA
来源
APPLIEDMATH | 2024年 / 4卷 / 02期
关键词
influenza; epidemic models; differential equations; basic reproduction number; partial immunity; DISEASE; TRANSMISSION; DYNAMICS; TIME;
D O I
10.3390/appliedmath4020036
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The basic reproduction, or reproductive number, is a useful index that indicates whether or not there will be an epidemic. However, it is also very important to determine whether an epidemic will eventually decrease and disappear or persist as an endemic. Different infectious diseases have different behaviors and mathematical models used to simulated them should capture the most important processes; however, the models also involve simplifications. Influenza epidemics are usually short-lived and can be modeled with ordinary differential equations without considering demographics. Delays such as the infection time can change the behavior of the solutions. The same is true if there is permanent or temporary immunity, or complete or partial immunity. Vaccination, isolation and the use of antivirals can also change the outcome. In this paper, we introduce several new models and use them to find the effects of all the above factors paying special attention to whether the model can represent an infectious process that eventually disappears. We determine the equilibrium solutions and establish the stability of the disease-free equilibrium using various methods. We also show that many models of influenza or other epidemics with a short duration do not have solutions with a disappearing epidemic. The main objective of the paper is to introduce different ways of modeling immunity in epidemic models. Several scenarios with different immunities are studied since a person may not be re-infected because he/she has total or partial immunity or because there were no close contacts. We show that some relatively small changes, such as in the vaccination rate, can significantly change the dynamics; for example, the existence and number of the disease-free equilibria. We also illustrate that while introducing delays makes the models more realistic, the dynamics have the same qualitative behavior.
引用
收藏
页码:666 / 689
页数:24
相关论文
共 54 条
[1]   Dynamics of a delayed plant disease model with Beddington-DeAngelis disease transmission [J].
Al Basir, Fahad ;
Takeuchi, Yasuhiro ;
Ray, Santanu .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2020, 18 (01) :583-599
[2]   A vaccination model for transmission dynamics of influenza [J].
Alexander, ME ;
Bowman, C ;
Moghadas, SM ;
Summers, R ;
Gumel, AB ;
Sahai, BM .
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS, 2004, 3 (04) :503-524
[3]  
Allen L. J. S., 2007, Introduction to mathematical biology
[4]   Mathematical Modeling and Numerical Simulation for the Outbreak of COVID-19 Involving Loss of Immunity and Quarantined Class [J].
Arif, Faiza ;
Majeed, Zain ;
Ul Rahman, Jamshaid ;
Iqbal, Naveed ;
Kafle, Jeevan .
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2022, 2022
[5]  
Bellen A., 2013, Numerical Methods for Delay Differential Equations
[6]   A Time Since Recovery Model with Varying Rates of Loss of Immunity [J].
Bhattacharya, Subhra ;
Adler, Frederick R. .
BULLETIN OF MATHEMATICAL BIOLOGY, 2012, 74 (12) :2810-2819
[7]   The SEIRS model for infectious disease dynamics [J].
Bjornstad, Ottar N. ;
Shea, Katriona ;
Krzywinski, Martin ;
Altman, Naomi .
NATURE METHODS, 2020, 17 (06) :557-558
[8]   Modeling the waning and boosting of immunity from infection or vaccination [J].
Carlsson, Rose-Marie ;
Childs, Lauren M. ;
Feng, Zhilan ;
Glasser, John W. ;
Heffernan, Jane M. ;
Li, Jing ;
Rost, Gergely .
JOURNAL OF THEORETICAL BIOLOGY, 2020, 497
[9]  
Centers for Disease Control, 2024, Influenza
[10]   The Incubation Periods of Dengue Viruses [J].
Chan, Miranda ;
Johansson, Michael A. .
PLOS ONE, 2012, 7 (11)