Mitigation Strategies for Foot and Mouth Disease: A Learning-Based Approach

Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning-based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.