Computational and statistical approaches for quantifying the role of multi-scale heterogeneity in Leishmania transmission dynamics

Leishmaniasis, a neglected tropical disease caused by infection with Leishmania parasites, affects millions of people annually across the globe. Leishmania transmission is facilitated by the sand fly vector, thus occurring across a range of climates with notable hotspots in Brazil and India. Its persistence despite ongoing eradication efforts underscores the importance of a complete understanding of the transmission dynamics in a range of environments. Developing this understanding requires tailored tools as the transmission dynamics are affected by heterogeneity at multiple scales, giving rise to a complex web of interactions. At the micro-scale, transmission is influenced by the heterogeneous parasite distributions of the host’s skin as well as the complex parasite life cycle in the sand fly and its link to sand fly biting behaviour. We derive and parameterise a simple model incorporating these factors, finding that their interactions give rise to unexpected transmission opportunities. The communities in which leishmaniasis typically propagates are highly heterogeneous but also ideal candidates for deploying network models. We test analytic estimates for two epidemiologically relevant quantities, the R0 (the average number of secondary infections caused by a single infected individual over their entire duration of infection) and the endemic equilibrium, in the context of heterogeneous networks. Although both prove to be unreliable for these structured communities, they have the potential to improve our understanding of when and where epidemics are likely to occur and be more severe. We also demonstrate the potential of using survival analysis to investigate medium and large-scale dynamics, first by confirming the role of distance in leishmaniasis transmissibility at the community level, and then by highlighting the role of social vulnerability in creating endemic hotspots. We then offer guidance for optimal application of survival analysis to future leishmaniasis research. Finally, these findings are synthesised with the wider literature to identify potential methodological improvements and further avenues of inquiry to further develop our knowledge of leishmaniasis transmission.