Objectives matter: A mathematical modelling framework to identify optimal control strategies for future infectious disease outbreaks

When deciding upon the optimal control intervention during an epidemic, a policy maker might seek to select the strategy that minimises a defined epidemic metric such as cost, duration or the number of severe disease cases. It is most likely, however, that the policy maker in question is required to consider multiple competing objectives which complicates the identification of the optimal strategy. The decision is further complicated by uncertainty, especially if the pathogen is novel and disease-specific parameters or the efficacy of interventions are unknown. The policy maker could therefore benefit from a framework which can recommend control strategies for future infectious disease outbreaks, subject to a cost function which includes multiple objectives. This project aims to develop a mathematical framework which uses a suitable objective function to explore optimisation of control interventions for infectious disease outbreaks. The project will use the framework to investigate how optimal intervention policies are dependent upon the spatiotemporal state of the outbreak, the characteristics of the disease and, crucially, the objective of policy makers when implementing such policies. This framework will be initially applied to models of respiratory virus outbreaks in humans of global significance, such as seasonal influenza. Time permitting, the framework will be extended to consider animal or plant diseases which may require an alternate modelling approach and a different class of control interventions. The project will rely upon the development and simulation of various models of infectious disease outbreaks whose projections will be used to measure the objective function against. The models must be developed to allow for flexible implementation of feasible control interventions and will be fitted to data (either provided by the external partner or publicly available through previous publications and/or online public health dashboards) using statistical inference, or incorporate plausible uncertainty in disease parameters such as the transmission rate to investigate changes in the objective function. We will develop a suite of models that can be used in a range of different future infectious disease outbreaks, including considering stochastic models and spatially explicit models. In the case of respiratory diseases, considering a spatially explicit model adds a layer of realism to the modelling as restrictions during the recent SARS-CoV-2 (COVID-19) pandemic were often localised. This can assist the policy maker in identifying vulnerable administrative divisions and taking targeted action. In the context of plant and animal diseases, the spatial heterogeneities are essential for model accuracy as the population can no longer be considered to be well-mixed.