My interests cover a wide range of disciplines, including evolutionary genetics, phylogeography, and epidemiology. In all of these fields I am interested in developing novel statistical approaches that help us to detect and characterise the key signals of interest amid a sea of randomness and noise. For example, I have worked extensively on spatial data, in which clusters of points may be indicative of some underlying process - such as hot-spots of infection in the case of disease data, or selectively favoured genes in the case of genetic data. Through the use of modern statistical methods, including Bayesian mixture models and Markov Chain Monte Carlo (MCMC), we can identify these hidden patterns in our data, leading to new insights, more efficient use of resources, and ultimately, lives saved.
As well as the spatial element of outbreak analysis, I am also interested in epidemiological modelling in a broader sense. In my current position I am using mathematical models to look into the cost-effectiveness and public health impact of the RTS,S malaria vaccine. The diversity of malaria settings worldwide makes choosing the 'best' intervention a challenge, and hence modelling is a key component in determining when and where any future vaccine is likely to be most effective. Through discussion with the WHO and our partner institutions it is hoped that our analyses will lead to informed decision making over the coming years.
For more info check out my website at www.bobverity.com
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et al., 2020, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, ISSN:0028-0836