Developing countries are faced with finding novel and humane ways to permanently reduce and control their dog population. Agent-based models developed to describe dog populations represent a unique, platform for using computer based simulation to identify control strategies with the greatest potential for success, aid in the design of more effective control measures, and provide a means to evaluate the success of different interventions.
In addition, these models can be combined with health economic models to not only examine the effectiveness of different intervention strategies but also the cost-effectiveness of these strategies. This presentation will introduce the basic agent-based modeling framework using AnyLogic software which allows for the creation of simulation models to describe the population dynamics of dogs within a given environment. We will provide modeling examples of simple dog population control strategies and demonstrate the utility of using simulation models to evaluate novel population control strategies while also incorporating economic considerations. We will highlight the data requirements for the development of such models as well as ways to add additional complexity to the models in order to Incorporate human decision-making and the transmission of zoonotic diseases (e.g. rabies).
Agent-based models offer the potential to help us better understand human-dog population dynamics and to advance the identification of successful dog population management initiatives. This novel methodology represents a unique opportunity to help inform evidence-based decision-making for dog population control and the prevention of dog-related zoonoses.