Spatio-temporal dynamics of plague vectors in Madagascar: using population genetics and landscape scale modelling to inform disease reduction strategi

Many emerging diseases are zoonotic (transmitted from animal to human), with human exposure typically higher in low income countries. Plague is one of the most infamous zoonoses, having been responsible for several pandemics. Today, Madagascar is a hot-spot for human plague cases, with approximately 40% of the world's annual cases. The zoonotic epidemiological cycle involves the black rat, Rattus rattus, and two species of flea. Following a large outbreak of pneumonic plague in 2017, there is increased interest in strategies aimed at reducing plague incidence, with rat control one option. However, where and when to control rats, and how different control options may impact on flea dynamics and disease transmission is still unclear.

Detailed knowledge of flea spatio-temporal dynamics and dispersal in the heterogeneous landscapes of Madagascar is lacking, but critical for our understanding of plague epidemiology and predictions of changing disease risk. Genetic and genomic data can give important information on population connectivity. Rat populations show weak genetic structure that increases with topographic relief. However, concordance between host and parasite genetic structure is not always found and no landscape-scale study of flea population genetics in Madagascar has been conducted. To aid management decisions, new information on the impact of landscape features on population connectivity of both rats and fleas needs to be combined with information on local population dynamics in a predictive modelling framework.

This project will combine advanced statistical analyses, population genetics and modelling, using existing data and samples to (1) examine how flea abundance is influenced by microhabitat and climatic conditions, (2) conduct a landscape genetic study of the two flea species and (3) use individual based modelling approaches to understand the landscape dynamics of rats and fleas and the impact of different control regimes. The modelling will be implemented in RangeShifter, which integrates population dynamics, dispersal behaviour and genetics, and can be used to simulate scenarios on spatially explicit landscapes.

The project will suit a student with a background in molecular ecology or population ecology, who has interests in spatial ecology, disease ecology, landscape genetics, and who has strong numerical skills. The student will be expected to work closely with the Institut Pasteur de Madagascar. The student will be given a thorough multidisciplinary training, integrating state of the art laboratory skills and statistical analysis of ecological and genetic data with computational modelling skills. The relative importance of the different project components will depend on the interests of the student.