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Models of infectious diseases in the presence of uncertainty - predicting risk and informing policy

 

Mathematical models are regularly used in the event of infectious disease outbreaks to predict future epidemic behaviour and the impact of intervention strategies. However, such models often have to make predictions when data are not readily available to inform model parameters. Despite this, policy makers require rapid advice to ensure that any control policy that is implemented can have the greatest effect possible. In this presentation, we will discuss the ability of livestock infectious disease models to make "robust" predictions in the absence of up to date epidemiological and demographic data. Our research specifically focuses upon adaptive management - a novel intervention strategy that "adapts" to accrual of information to ensure that the control strategy that is implemented is optimal, given the information that is available at that point in the outbreak. Such a strategy can reduce the economic impact of future outbreaks of livestock infectious disease.