In this paper, we introduce and analyze a discrete–time model of an epidemic spread in a heterogeneous population. As the heterogeneous population, we define a population in which we have two groups which differ in a risk of getting infected: a low–risk group and a high–risk group. We construct our model without discretization of its continuous–time counterpart, which is not a common approach. We indicate functions that reflect the probability of remaining healthy, which are based on the mass action law. Additionally, we discuss the existence and local stability of the stability states that appear in the system. Moreover, we provide conditions for their global stability. Some of the results are expressed with the use of the basic reproduction number $ \mathcal{R}_0 $. The novelty of our paper lies in assuming different values of every coefficient that describe a given process in each subpopulation. Thanks to that, we obtain the pure population's heterogeneity. Our results are in a line with expectations – the disease free stationary state is locally stable for $ \mathcal{R}_0 < 1 $ and loses its stability after crossing $ \mathcal{R}_0 = 1 $. We supplement our results with a numerical simulation that concerns the real case of a tuberculosis epidemic in Poland.
Citation: Marcin Choiński. An inherently discrete–time $ SIS $ model based on the mass action law for a heterogeneous population[J]. Mathematical Biosciences and Engineering, 2024, 21(12): 7740-7759. doi: 10.3934/mbe.2024340
In this paper, we introduce and analyze a discrete–time model of an epidemic spread in a heterogeneous population. As the heterogeneous population, we define a population in which we have two groups which differ in a risk of getting infected: a low–risk group and a high–risk group. We construct our model without discretization of its continuous–time counterpart, which is not a common approach. We indicate functions that reflect the probability of remaining healthy, which are based on the mass action law. Additionally, we discuss the existence and local stability of the stability states that appear in the system. Moreover, we provide conditions for their global stability. Some of the results are expressed with the use of the basic reproduction number $ \mathcal{R}_0 $. The novelty of our paper lies in assuming different values of every coefficient that describe a given process in each subpopulation. Thanks to that, we obtain the pure population's heterogeneity. Our results are in a line with expectations – the disease free stationary state is locally stable for $ \mathcal{R}_0 < 1 $ and loses its stability after crossing $ \mathcal{R}_0 = 1 $. We supplement our results with a numerical simulation that concerns the real case of a tuberculosis epidemic in Poland.
[1] | H. W. Hethcote, Three basic epidemiological models, in Applied Mathematical Ecology, Springer Berlin Heidelberg, Berlin, Heidelberg, (1989), 119–144. https://doi.org/10.1007/978-3-642-61317-3_5 |
[2] | S. Jain, S. Kumar, Dynamic analysis of the role of innate immunity in SEIS epidemic model, Eur. Phys. J. Plus, 136 (2021), 439. https://doi.org/10.1140/epjp/s13360-021-01390-3 doi: 10.1140/epjp/s13360-021-01390-3 |
[3] | H. Mohajan, Mathematical analysis of SIR model for COVID–19 transmission, J. Innovations Med. Res., 1 (2022), 1–18. |
[4] | O. N. Bjørnstad, K. Shea, M. Krzywiński, A. Altman, The SEIRS model for infectious disease dynamics, Nat. Methods, 17 (2020), 557–558. https://doi.org/10.1038/s41592-020-0856-2 doi: 10.1038/s41592-020-0856-2 |
[5] | R. Farnoosh, M. Parsamanesh, Disease extinction and persistence in a discrete-time $SIS$ epidemic model with vaccination and varying population size, Filomat, 31 (2017), 4735–4747. https://doi.org/10.2298/FIL1715735F doi: 10.2298/FIL1715735F |
[6] | B. Tumurkhuyag, B. Batgerel, Nonstandard finite difference scheme for the epidemic model with vaccination, J. Math. Sci., 279 (2024), 841–849. https://doi.org/10.1007/s10958-024-07064-6 doi: 10.1007/s10958-024-07064-6 |
[7] | R. E. Mickens, Nonstandard Finite Difference Models of Differential Equations, World Scientific, 1993. |
[8] | M. Martcheva, An introduction to mathematical epidemiology, in Texts in Applied Mathematics, 61 (2015). https://doi.org/10.1007/978-1-4899-7612-3 |
[9] | D. Castillo-Chavez, A. A. Yakubu, Discrete–time S-I-S models with complex dynamics, Nonlinear Anal. Theory Methods Appl., 47 (2001), 4753–4762. https://doi.org/10.1016/S0362-546X(01)00587-9 doi: 10.1016/S0362-546X(01)00587-9 |
[10] | J. E. Franke, A. A. Yakubu, Discrete-time SIS epidemic model in a seasonal environment, Soc. Ind. Appl. Math., 66 (2006), 1563–1587. https://doi.org/10.1137/050638345 doi: 10.1137/050638345 |
[11] | R. Bravo de la Parra, Reduction of discrete-time infectious disease models, Math. Methods Appl. Sci., 46 (2021), 1–19. https://doi.org/10.1002/mma.9186 doi: 10.1002/mma.9186 |
[12] | B. Mathema, J. R. Andrews, T. Cohen, M. W. Borgdorff, M. Behr, J. R. Glynn, et al., Drivers of tuberculosis transmission, J. Infect. Dis., 216 (2017), 644–653. https://doi.org/10.1093/infdis/jix354 doi: 10.1093/infdis/jix354 |
[13] | M. Choiński, A discrete SIS model of epidemic for a heterogeneous population without discretization of its continuous counterpart, Adv. Sci. Technol. Res. J., 17 (2023), 288–300. https://doi.org/10.12913/22998624/174335 doi: 10.12913/22998624/174335 |
[14] | M. Bodzioch, M. Choiński, U. Foryś, $SIS$ criss-cross model of tuberculosis in heterogeneous population, Discrete Contin. Dyn. Syst. - Ser. B, 24 (2019), 2169–2188. https://doi.org/10.3934/dcdsb.2019089 doi: 10.3934/dcdsb.2019089 |
[15] | P. van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6 |
[16] | Central Statistical Office of Poland, Statistical Yearbooks, 2024. Available from: https://stat.gov.pl/en/topics/statistical-yearbooks/. |
[17] | W. H. Lai, S. L. Kek, T. K. Gaik, Solving nonlinear least squares problem using Gauss-Newton method, Int. J. Innovative Sci. Eng. Technol., 4 (2015), 258–262. |