Dynamic zoonotic disease modelling for environmental change

Human infectious diseases are a significant threat to global human health and economies (e.g., Ebola, SARS), with the majority of infectious diseases having an animal source (zoonotic) (Jones et al. 2008 Nature 451:990). Despite their public health relevance, many important diseases have not been systematically studied from a quantitative perspective, limiting our understanding of how spillovers of zoonotic infectious diseases into the human population are impacted by global and local environmental stressors (Whitmee et al. 2015 Lancet 386:1973). Furthermore, for most diseases little is known about how climate change, anthropogenic landscape alteration and changing populations will impact on future infectious disease outbreaks (Hotez & Kamath 2009 PLoS Negl Trop Dis 3:e412). There is therefore an urgent need for a more interdisciplinary approach integrating computational modelling, ecology and health towards a holistic understanding of disease dynamics.

Computational modelling can play a significant role in prediction of potential threats and evaluation of intervention strategies, yet effective modelling of zoonotic diseases requires an interdisciplinary breadth that is seldom achieved. In this project, we bring together leading computational, ecological and epidemiological expertise to develop a new integrated framework of disease modelling for the important endemic Lassa fever (LF) disease in West Africa. Lassa fever virus is the cause of one of the most prevalent viral haemorrhagic fevers in the region. Serology-based techniques estimate between 100,000 to 300,000 LF cases per year (compare to Ebola 30,000 total cases since 1970), with a fatality rate that ranges between 1% and 69% depending on the setting. Despite its importance, relatively few studies have tackled the problem of modelling the epidemiological dynamics of LF on a large scale (Redding et al. 2016 Methods in Ecol. & Evol 7:646). As in most zoonotic diseases, much of the complexity arises from the interplay of disease dynamics in reservoir (animal) populations and the human population, as well as the spillover between the reservoir and humans. All of these processes happen concurrently in a spatially heterogeneous and dynamic environment; understanding the origin of spatially localised phenomena such as hotspots is essential to predict effects of environmental change and evaluate intervention strategies.
Our ambition in this PhD project is to combine ecological models of animal reservoir, spillover and human disease spread in a single probabilistic modelling framework which will enable full uncertainty quantification and prediction. Importantly, our approach will leverage recent developments in the statistical computing community (Schnoerr et al. 2016 Nature Comms 7) to retain a higher level of mechanistic detail that is currently possible within epidemiological models. This will enable us to provide model-based predictions of responses to a changing environment, and to evaluate the impact of intervention strategies in plausible future scenarios. This project will work in close collaboration with the Institute of Global Health at UCL and partners at The Centre for Disease control for Nigeria and the west African hub of the African Union to embed the research into policy and priority setting within the stakeholder communities across west Africa.