Inference on a stochastic SIR model including growth curves

A Susceptible-Infected-Removed stochastic model is presented, in which the stochasticity is introduced through two independent Brownian motions in the dynamics of the Susceptible and Infected populations. To account for the natural evolution of the Susceptible population, a growth function is considered in which size is influenced by the birth and death of individuals. Inference for such a model is addressed by means of a Quasi Maximum Likelihood Estimation (QMLE) method. The resulting nonlinear system can be numerically solved by iterative procedures. A technique to obtain the initial solutions usually required by such methods is also provided. Finally, simulation studies are performed for three well-known growth functions, namely Gompertz, Logistic and Bertalanffy curves. The performance of the initial estimates of the involved parameters is assessed, and the goodness of the proposed methodology is evaluated. • A generalized Susceptible-Infected-Removed stochastic model is considered including a generic growth function for the Susceptible population. • An inference procedure is provided for several choices of the Susceptible growth function. • A regression-based approach is provided in order to find initial solutions for numerical methods performing the ML estimates.