Statistical inference and planning for complex infectious disease systems

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Infectious diseases are of great importance across society. Outbreaks of diseases such as pandemic H1N1 influenza, Ebola, SARS, or foot-and-mouth disease pose direct threats to public health and/or have serious economic effects. There diseases can even by used as weapons, introduced as acts of bio- or agro-terrorism.****In order to control such diseases, it is vital to understand how the disease spreads over time and what factors lead to some individuals becoming infected and some not. To this end, a series of mathematical 'individual-based' or 'individual-level' models for infectious disease transmission has been developed in recent years. Such models are intuitive, flexible, and have been shown to accurately describe the patterns of previously observed epidemics over space and time. Of key importance is the fact that they allow information about the individuals in the population, such as spatial location, where they work, their genetic information, etc., to be included in the model. This is in contrast to classic infectious disease transmission models that assume that everybody in the population is the same, and everybody in the population comes into contact with everybody else equally often.***Statistical inference is a process used to derive and assess models that are informed by observed data. It is advantageous to incorporate data collected from the real world into our models, as it produces models that better reflect reality. Thus, we can have better confidence in conclusions drawn from our models.***Computationally intensive techniques are used to carry out this inference process. However, when applied to these complex individual-level models, these intensive techniques can take the computer a long time to carry out, especially if we have a lot of data, lots of missing data, or inaccuracies in the data that we wish to account for; all are typically the case with infectious disease data sets. This can be a major problem if results are needed very quickly. For example, we may wish to decide how best to control a disease during the course of an outbreak, and so require data analysis to be carried out as quickly as possible.***The purpose of this research is therefore to do the following: 1) develop these individual-level models so they can more accurately and reliably model disease transmission as it occurs in real life (e.g., by using genetic information collected on the virus or bacteria itself); 2) improve the computationally intensive statistical inference process to make it more efficient; 3) use these models to help design animal disease transmission experiments, the purpose of which is to collect informative data in a controlled environment to aid understanding disease transmission and how it might be treated and controlled.***These developments can then be used to further our understanding of infectious diseases, and through this, our ability to control them or alleviate unnecessarily severe outcomes.**