Background: The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With domestic dogs being a principal reservoir host of Leishmania infantum, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. The intention being that limiting prevalence in this reservoir will result in a reduced risk of infection for the human population. One way this can be achieved is through the use of mathematical models.
Methods: We have developed a stochastic, spatial, individual-based mechanistic model of Leishmania infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.
Results: We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of Leishmania infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with Leishmania infantum parasites.
Conclusions: Establishing the model parameters with the highest sensitivity of average Leishmania infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.
Elizabeth Buckingham-Jeffery *, Edward M. Hill*, Samik Datta, Erin Dilger, Orin Courtenay (2018)
Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil.
* denotes equal contributors