Applying optimal control theory to complex epidemiological models to inform real-world disease management.
Philos Trans R Soc Lond B Biol Sci
The Royal Society
MetadataShow full item record
Bussell, E., Dangerfield, C., Gilligan, C., & Cunniffe, N. (2019). Applying optimal control theory to complex epidemiological models to inform real-world disease management.. Philos Trans R Soc Lond B Biol Sci, 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. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
disease management, feedback, model predictive control, optimal control, Communicable Disease Control, Computer Simulation, Disease Outbreaks, Humans, Models, Biological
Biotechnology and Biological Sciences Research Council (1643594)
External DOI: https://doi.org/10.1098/rstb.2018.0284
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288396
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: firstname.lastname@example.org