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SEIRS epidemics with disease fatalities in growing populations. (English) Zbl 1380.92065

Summary: An SEIRS epidemic with disease fatalities is introduced in a growing population (modelled as a super-critical linear birth and death process). The study of the initial phase of the epidemic is stochastic, while the analysis of the major outbreaks is deterministic. Depending on the values of the parameters, the following scenarios are possible. i) The disease dies out quickly, only infecting few; ii) the epidemic takes off, the number of infected individuals grows exponentially, but the fraction of infected individuals remains negligible; iii) the epidemic takes off, the number of infected grows initially quicker than the population, the disease fatalities diminish the growth rate of the population, but it remains super critical, and the fraction of infected go to an endemic equilibrium; iv) the epidemic takes off, the number of infected individuals grows initially quicker than the population, the diseases fatalities turn the exponential growth of the population to an exponential decay.

MSC:

92D30 Epidemiology
92D25 Population dynamics (general)

Software:

deSolve; diffEq

References:

[1] Allen, L. J.; Lahodny Jr., G., Extinction thresholds in deterministic and stochastic epidemic models, J. Biol. Dyn., 6, 590-611 (2012) · Zbl 1447.92388
[2] Ball, F.; Britton, T.; Sirl, D., A network with tunable clustering, degree correlation and degree distribution, and an epidemic thereon, J. Math. Biol., 66, 4, 979-1019 (2013) · Zbl 1258.92031
[3] Ball, F.; Britton, T.; House, T.; Isham, V.; Mollisone, D.; Pellis, L.; Tomba, G. S., Seven challenges for metapopulation models of epidemics, including households models, Epidemics, 10, 63-67 (2015)
[4] Brauer, F., An introduction to networks in epidemic modeling, Mathematical Epidemiology, Lecture Notes in Mathematics, 1945, 133-145 (2008), Springer-Verlag: Springer-Verlag Berlin · Zbl 1206.92024
[5] Britton, T.; Trapman, P., Stochastic epidemics in growing populations, Bull. Math. Biol., 76, 985-996 (2014) · Zbl 1297.92074
[6] Britton, T., Stochastic epidemic models : a survey, Math. Biosci., 225, 24-35 (2010) · Zbl 1188.92031
[7] Colizza, V.; Vespignani, A., Epidemic modeling in metapopulation system with heterogeneous coupling pattern: theory and simulations, J. Theor. Biol., 251, 450-467 (2008) · Zbl 1398.92233
[8] Coburn, B. J.; Wagner, B. G.; Blower, S., Modeling influenza epidemics and pandemics: insights into the future of swine flu (h1n1), BMC Med., 7, 1, 30 (2009)
[9] Diekmann, O.; Heesterbeek, H.; Britton, T., Mathematical Tools for Understanding Infectious Disease Dynamics (2013), Princeton University Press · Zbl 1304.92009
[10] Diekmann, O.; Heesterbeek, H.; Roberts, M. G., The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface, 7, 873-885 (2010)
[11] Greenhalgh, D., Hopf bifurcation in epidemic models with a latent period and non permanent immunity, Math. Comput. Model., 25, 2, 85-107 (1997) · Zbl 0877.92023
[12] Haaneim, D. R.; Stein, F. M., Methods of solution of the riccati differential equation, Math. Mag., 42, 5, 233-240 (1969) · Zbl 0188.15002
[13] Haccou, P.; Jagers, P.; Valutin, V. A., Branching Processes: Variation, Growth, and Extinction of Populations (2005), Cambridge University Press: Cambridge University Press Cambridge · Zbl 1118.92001
[14] Hirsh, M. W.; Smale, S.; Devaney, R. L., Differential Equations, Dynamical Systems and an Introduction to Chaos (2004), Elsivier Academic Press · Zbl 1135.37002
[15] L.B.e. Chiffres (ed.), 2011. Institut National de la Statistique et de la Démographie.; L.B.e. Chiffres (ed.), 2011. Institut National de la Statistique et de la Démographie.
[16] Jagers, P., Branching Processes with Biological Applications (1975), Wiley: Wiley New York · Zbl 0356.60039
[17] Kumar, A.; Srivastava, P. K., Vaccination and treatment as control interventions in an infectious disease model with their cost optimization, Commun. Nonlinear Sci. Numer. Simul., 44, 334-343 (2017) · Zbl 1465.92061
[18] Miller, J. C., Epidemic size and probability in populations with heterogeneous infectivity and susceptibility, Phys. Rev. E., 76 (2007)
[19] Mills, C. E.; Robins, J. M.; Lipsitch, M., Transmissibility of 1918 pandemic influenza, Nature, 432 (2004)
[20] Routh, E. J., A Treatise on the Stability of a Given State of Motion: Particularly Steady Motion (1877), Macmillan and Company
[21] Soetaert, K.; Petzoldt, T.; Setzer, R. W., Solving differential equations in r: package desolve, J. Stat. Softw., 33, 9, 1-25 (2010)
[22] 2003. World Health Organization (WHO). Influenza. Fact sheet \(N^o\)https://www.who.int/mediacentre/factsheets/2003/fs211/en/; 2003. World Health Organization (WHO). Influenza. Fact sheet \(N^o\)https://www.who.int/mediacentre/factsheets/2003/fs211/en/
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