Statistical inference with mechanistic models on heterogeneous data: improving the control of infectious diseases

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Influenza epidemics occur every year, resulting in large amounts of illness in the community, many early deaths, major disruption to the health services, and significant economic losses. This is despite widespread vaccination. Although the vaccine contains three strains, it is not possible to say ahead of the epidemic which (if any) of the strains will circulate, and how severe will the resultant epidemic be. This means that planning by public health authorities, physicians, and hospitals is difficult, resulting in significant inefficiencies (such as the unnecessary cancelling elective surgeries, etc). The size of an influenza epidemic is governed, amongst other things, by the level of immunity in the population (it is for this reason that pandemics are so feared, as the novel virus tends to be very different from existing strains, and so the level of immunity in the population is low). The emergence of a novel H1N1 (swine flu) virus in 2009 has been closely monitored and studied. The UK has one of the best influenza surveillance systems in the world, and has amassed a great deal of data on the spread, severity, and population immunity to this virus. Despite this, the virus has surprised public health officials and mathematical modellers alike, as a significant epidemic was observed during the winter of 2010/11 despite apparently high levels of population immunity. What may happen in the coming years is equally unknown. The emergence of this virus and the wealth of data available provide an unique opportunity to better understand the dynamics of a new influenza virus following its introduction into the human population. We intend to develop and test a number of different mathematical models to build a better picture of the dynamics and evolution of influenza in the population. The models will be fitted to the range of epidemiological data using state-of-the art statistical techniques, which will have general applicability within the fields of infectious disease dynamics and statistical inference. The statistical framework will shed light on the effective level of protection in the population against subsequent drifted variants, and pave the way for the next generation of predictive tools. These investigation are critical to improve the effectiveness of public health measures, like vaccination, and determine which data should be prioritised to help make predictive models of seasonal and pandemic influenza.This multi-disciplinary project involves many different stakeholders, including the bodies that are collecting the data, experts in disease transmission and host-pathogen interactions, mathematical modellers who formalize biological mechanisms, statisticians who develop rigorous and robust methods to confront models to data, and finally, public health experts who ask the questions that the model must address. It is envisaged that the project will help improve public health policy in this high-profile area, develop new methods for fitting models to data, and provide an ideal training ground for the lead applicant to become an established leader in mathematical epidemiology.