Background.

One of the reasons to study infectious diseases is to increase their control and eradication. Mathematical models could be a powerful tool to achieve that end, which allows for optimization of limited resources, and conduct more effective measures; these models are made to predict and increase understanding of the studied phenomenon. The aim of this paper is to conduct simulations using the stochastic model known as Susceptible, Infected, Recovered (SIR), and introduce them in the curricula of students, epizootiological analyses, and decision-making.

Methods.

Virtually the entrance of ten migrating birds with respiratory processes was simulated on bird rearing farms averaging 5000 free-range poultry from different types. The R0 was determined depending on the incidence.

Results.

Simulation was run with a low communicability index (1.8 %), that is, 1.8 out of every 100 birds get sick, after interacting with other 5 birds each, at random, for 10 days. In the absence of intervention, there would be a spike of sick animals at 20 days, surpassing 3000 birds. A basic reproductive number greater than the unit (R0=1.14) was observed, thus qualifying as an epidemic outbreak.

Conclusions.

A number of simulations were made using the stochastic model with a sustainable analytical tool based on an R-free program. The need to integrate the epizootiology, epidemiology, and mathematic was demonstrated, suggesting that appropriate training should be provided by competent professionals.