Emerging infectious diseases are still serious impending threats to global health, demonstrated by outbreaks of SARS coronavirus, Zaire ebolavirus, and Zika virus, among others over the last twenty years. I use a variety of statistical methods including machine learning and Markov models to investigate the processes surrounding disease emergence and how pathogens jump from one host to another. These models bring together pathogens, hosts and the wider ecological context – supporting the idea that these are fundamentally connected as ‘One Health’. Ultimately, my research aims to understand where the risks of future emerging pathogens lie and contribute to a preventive public health strategy.
Dynamics of emergence of human RNA viruses
Human RNA viruses are highly diverse in their origins and epidemiology within human hosts. In my work with Epigroup, I continually maintained data on all known and newly recognised human viruses and their traits. I use this data to investigate dynamics of viral emergence in several key areas, for example, ecological predictors of virulence in humans and how human-to-human transmissibility develops following cross-species transmission.
Influence of host ecology on viral epidemiology
The majority of our pathogens are shared with animals, particularly viruses. I am especially interested in how both diversity and life history of wild and domestic animal hosts influences the evolution and resulting epidemiological dynamics of human viruses.
Global drivers of emerging infectious diseases
Emerging infectious diseases have been linked to the increasingly rapid changes to landscapes and the increased pressure of globalisation. Working with the Biodiversity Modelling Research Group at UCL, I have used spatial models to identify global hotspots of zoonotic bat virus risk, and how this risk is shaped by viral diversity and human activity such as bushmeat hunting.