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On the role of vector modeling in a minimalistic epidemic model

1 Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, ul. Akademik Georgi Bonchev 8, 1113 Sofia, Bulgaria
2 Dipartimento di Matematica “Giuseppe Peano”, Universit`a di Torino, via Carlo Alberto 10, 10123 Torino, Italy; Member of the INdAM research group GNCS
3 Dipartimento di Matematica, Universit`a degli Studi di Trento, via Sommarive 14, 38123 Povo Trento, Italy
4 Faculdade de Ciˆencias da Universidade de Lisboa, Campo Grande, C6-Piso 1, Gabinete C6.1.18, 1749-016 Lisboa, Portugal
5 Faculty of Science, VU University, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands

Special Issues: Mathematical Methods in the Biosciences

The motivation for the research reported in this paper comes from modeling the spread of vector-borne virus diseases. To study the role of the host versus vector dynamics and their interaction we use the susceptible-infected-removed (SIR) host model and the susceptible-infected (SI) vector model. When the vector dynamical processes occur at a faster scale than those in the host-epidemics dynamics, we can use a time-scale argument to reduce the dimension of the model. This is often implemented as a quasi steady-state assumption (qssa) where the slow varying variable is set at equilibrium and an ode equation is replaced by an algebraic equation. Singular perturbation theory will appear to be a useful tool to perform this derivation. An asymptotic expansion in the small parameter that represents the ratio of the two time scales for the dynamics of the host and vector is obtained using an invariant manifold equation. In the case of a susceptible-infected-susceptible (SIS) host model this algebraic equation is a hyperbolic relationship modeling a saturated incidence rate. This is similar to the Holling type II functional response (Ecology) and the Michaelis-Menten kinetics (Biochemistry). We calculate the value for the force of infection leading to an endemic situation by performing a bifurcation analysis. The effect of seasonality is studied where the force of infection changes sinusoidally to model the annual fluctuations of the vector population. The resulting non-autonomous system is studied in the same way as the autonomous system using bifurcation analysis.
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