15–19 juin 2026
Institut de Mathématiques de Toulouse
Fuseau horaire Europe/Paris

Stochastic Epidemic Model for Malaria: the Law of Large Numbers

15 juin 2026, 16:30
15m
Amphithéâtre Schwartz (Institut de Mathématiques de Toulouse)

Amphithéâtre Schwartz

Institut de Mathématiques de Toulouse

Université Toulouse 3 Paul Sabatier 23 rue sebastienne guyot Institut de Mathématiques- Bâtiment 1R3 Toulouse

Orateur

Othmane Baghdadi (Université Mohammed Premier Oujda, Marrocco)

Description

We study an individual-based stochastic host–vector epidemic model for malaria, in which humans can experience repeated infections over their lifetime. In contrast to classical Ross–Macdonald or compartmental SIR/SEIR models, each infection episode is characterised by a random time-dependent infectivity profile: after infection, a human host transmits parasites to susceptible mosquitoes according to a random infectivity function of the time since infection, while recovered hosts gradually regain susceptibility according to a random susceptibility function. On the vector side, susceptible mosquitoes become infected through contact with infectious humans and then contribute to transmission until death, under a demographic regime that combines birth and mortality processes.

We analyse the large-population asymptotic behaviour of this coupled host–vector system and prove a functional law of large numbers (FLLN) by constructing a sequence of i.i.d. auxiliary processes. The limiting dynamics are described by a nonlinear deterministic system of renewal-type integral equations that generalises both the classical Kermack–McKendrick age-of-infection framework and standard malaria models. In this limit, the solution of the limiting deterministic system depends on the expectation of a complicated functional of the random susceptibility functions, but only on the mean infectivity functions of humans and mosquitoes.

Othmane Baghdadi¹ and Étienne Pardoux²
¹ Mohammed First University, Oujda, Morocco — othmane.baghdadi.d23@ump.ac.ma
² Aix-Marseille University, CNRS, I2M, 13453 Marseille, France — etienne.pardoux@univ-amu.fr

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