Bishop, Alex (2020) Balancing model complexity and inferential capability for disease modelling. PhD thesis, University of Warwick.

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Abstract

Mathematical models for study of infectious diseases have a rich history but it is only in recent years that directly fitting highly complex models to data has become possible. This has lead to a quantum leap in the capabilities of mathematical modelling for contributing to an evidence base for policy decisions. There still remains a large gap between the most complex models we can simulate and the most complex models we can perform inference on resulting in a trade-off between model complexity and inferential capability. This thesis tackles three separate problems with this trade-off in mind.

First, we study the dynamics of epidemics on degree heterogeneous clustered networks. Network models have many attractions but possess drawbacks such as one must generally resort to stochastic simulation for clustered networks, which represent realistic societal structure, as closed-form approximations of the dynamics do not hold in the highly clustered regime. Furthermore, data for these systems are hard to collect as they must measure the pairwise interactions of each individual - this lack of data limits the possibilities for applying inference. We develop a new model not requiring extensive simulations that approximates these dynamics more accurately than previous approaches, thus improving on the first problem mentioned.

Second, across several chapters we analyse the role of household structure in the transmission and control of soil-transmitted helminths (STH). Starting with a hierarchical negative binomial regression for which inference can easily be performed but which neglects the non-independence of observations, we move on to develop a general methodology for constructing and performing Bayesian inference on stochastic household models that consider different transmission dynamics within and between the households in a population. This permits us to estimate the extent to which transmission occurs within, compared to between, households and simulate the effectiveness of various control strategies – with some exploiting the household structure. The limits to which this general methodology may be extended to arbitrary demographic classes and infection levels before inference with exact-likelihood methods no longer become computationally feasible is explored.

Finally, we build a model of the global control programme for lymphatic filariasis at a regional level, forecasting the number of treatments required each year and their costs in order to reach elimination. A scenario where existing guidelines remained in place and a scenario where proposed guidelines incorporating a new treatment were considered. Our analysis was used by WHO as part of the evidence base for adopting precisely these new guidelines.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Library of Congress Subject Headings (LCSH):	Communicable diseases -- Mathematical models, Communicable diseases -- Transmission -- Mathematical models, Epidemics -- Mathematical models, Helminths, Stochastic processes
Official Date:	April 2020
Dates:	Date Event April 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Centre for Complexity Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hollingsworth, T. Déirdre ; House, Thomas A.
Format of File:	pdf
Extent:	xxiv, 168 leaves : illustrations (some colour)
Language:	eng

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