Applying optimal control theory to complex epidemiological models to inform real-world disease management.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
The Royal Society
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Bussell, E., Dangerfield, C., Gilligan, C., & Cunniffe, N. (2019). Applying optimal control theory to complex epidemiological models to inform real-world disease management.. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 374 (1776), 20180284. https://doi.org/10.1098/rstb.2018.0284
Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially-explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach -- optimal control theory -- allows the best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of humans, animals and plants.
Humans, Disease Outbreaks, Communicable Disease Control, Models, Biological, Computer Simulation
External DOI: https://doi.org/10.1098/rstb.2018.0284
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288396