BBSRC EASTBIO DTP: Dynamic modelling of foot-and-mouth disease epidemiology and persistence in endemic areas

Increasingly it is clear that to understand the ecology and epidemiology of pathogens we also need to understand and incorporate their evolution in models. This is particularly critical for rapidly evolving viruses such as foot-and-mouth disease in animals or influenza in humans (Roche, Drake, & Rohani, 2011). This is important because simple differential equation models that assume random mixing and fixed pathogen characteristics are likely to be biased and over estimate the impact of controls such as vaccination and movement restrictions. Furthermore, most current animal disease models are largely defined at a herd level and rarely include within herd dynamics or account for the free ranging movements seen in communal grazing areas such as in Africa. Therefore to understand how disease control might work at large geographical scales such as in sub-Saharan Africa (SSA) it is important to understand the how pathogens are evolving to evade the immune system of a host and how that intersects with host immunity waning or the accumulation of new naïve animals in the population through births. Understanding of these effects will become essential when attempting large scale disease eradication as currently being considered by the Gates Foundation, FAO and OIE for foot-and-mouth disease (Kitching et al., 2007). Unlike smallpox and rinderpest, where there was a single vaccine that produced life long immunity against all strains, in the case of foot-and-mouth disease there are multiple serotypes (6 currently known to circulate in SSA) and strains and current vaccines induce short lived immunity with little or no cross protection resulting in the need for regular vaccine matching. The overlap between epidemiological and evolutionary time scales, the observed immune drift in the livestock populations, and the high mutation rate of RNA viruses make it essential to integrate a large strain space into models. Consequently, the classic modeling approaches, based on the familiar SIR framework, are not readily amenable for this purpose because the resulting state space increases rapidly with the number of strains and, consequently, becomes too cumbersome for meaningful analysis. Therefore using FMD as an example this project will develop an agent based modelling framework. The rationale of this kind of modelling is to explore individual heterogeneity, allowing us to track infection history. This framework will include movement networks and virus evolution layers over laid on the transmission model to explore these dynamics in silico to develop the theoretical bases from which specific control options can be explored. These type of models are now possible because of the availability of cheap processing power.

The project will focus on two key and interlinked questions in RNA viral epidemiology using FMD as the exemplar: i) at what scale(s) do viruses persist in an endemic setting and ii) what are the drivers of persistence (local or long-range livestock movement, virus evolution, waning host immunity, population size, carrier state etc)?

The project builds on the supervisors long experience of working on FMD in Africa and in modelling disease spread (Bronsvoort, 2003). In addition we have on going work in Cameroon where much of the original work on endemic FMD was carried out, describing livestock movements through markets and using GPS collars to map within, between herd and transhumance movements. Previous phylogenetic analyses of FMD spread have informed analyses of the spatial spread of the virus and evolutionary changes but have not explicitly incorporated these in consideration of local spread. Furthermore, recent work on identifying when past exposure to an infection will protect against newly emerging strains is likely to be critical to viral persistence at some scale but remains to be fully understood and incorporated in models of viral spread. The modelling framework developed here will have significant benefits for planners and national governments in developing regional strategies for vaccination and other control strategies for viral diseases.

Applications including a full CV with names and addresses (including email addresses) of two academic referees, should be sent to: Liz Archibald, Postgraduate Research Student Administration, The Roslin Institute and R(D)SVS, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG. Or emailed to [Email Address Removed]. When applying for the studentship please state clearly the title of the studentship and the supervisors in your covering letter.