A system dynamics model for disease management in poultry production

Abstract

The objective of this article was to provide the nonmodeler reader of Poultry Science, an overview of the system dynamics modeling method (SDM) through development of a broiler house disease management simulator (BHDMS). System dynamics modeling uses feedback theory and computer-aided simulation to help elucidate relationships between factors in complex systems, which may be circular or interrupted with long delays. Materials used to build the simulator include data from literature and industry indices. The methods used were the steps in SDM, namely: 1) Identify the problem and boundaries; 2) develop a dynamic hypothesis explaining cause of the problem; 3) build the causal loop diagram (CLD); 4) develop the stock and flow model; 5) conduct model simulations; and 6) model validation. Results presented here are the CLD and stock and flow model of the simulator, results of scenario simulations, and model validity tests. The simulator consists of the main model, the disease submodel, and the antimicrobial use submodel. The main model represents a cycle of production in the broiler house of a specified length of time, which repeats after a specified down time. The disease submodel shows population dynamics in the broiler house in terms of changes over time in number of susceptible, infected, recovered, and dead birds. Production parameters that could be modified in the model include delivery size, grow-out period, down time, and efficacy of antimicrobials. Disease mortality levels, above the set threshold, trigger antimicrobial use in the model. The model showed the effect of antimicrobial use intervention on the population dynamics, namely, on the proportion of the susceptible, infected, recovered, and dead birds in the population. Thus, the BHDMS was able to simulate the effect of the intervention on population dynamics and would facilitate evaluating management interventions such as antimicrobial use.