Work

Inference on a stochastic SIR model including growth curves

Year: 2025

Type: article

Abstract: 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.

Source: Computational Statistics & Data Analysis

DOI: 10.1016/j.csda.2025.108231

Authors: Giuseppina Albano, Virginia Giorno, Gema Pérez-Romero, Francisco de Asis Torres-Ruiz

Institutions: University of Salerno, Universidad de Granada

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Estimated APC paid: $3,540

Funders: Ministerio de Ciencia e Innovación, Ministero dell'Università e della Ricerca, Gruppo Nazionale per il Calcolo Scientifico