Data for Policy recommendations from transmission modelling for the elimination of visceral leishmaniasis in the Indian subcontinent

Methods

This paper draws together the key policy-relevant recommendations from recent transmission modelling of VL, and presents new predictions for VL incidence under the control interventions recommended by the WHO using the latest transmission models. Results The model predictions suggest that the current WHO guidelines should be sufficient to reach the elimination target in areas that had medium VL endemicities (up to 5 VL cases per 10,000 population per year) prior to the start of interventions.

However, additional interventions, such as extending the WHO attack phase (intensive IRS and ACD), may be required to bring forward elimination in regions with high pre-control endemicities, depending on the relative infectiousness of different disease stages. Conclusions The potential hurdle that asymptomatics, and in particular post-kala-azar dermal leishmaniasis cases (PKDL), may pose to reaching and sustaining the target needs to be addressed.

As VL incidence decreases, the pool of immunologically naive individuals increases, which creates the potential for new large-scale outbreaks.<br><br>MATLAB code for running simulations of Warwick transmission models Description: MATLAB code for running simulations of the World Health Organization’s guidelines for visceral leishmaniasis (VL) elimination in the Indian subcontinent (described in [1]), for the Warwick VL ordinary differential equation transmission models described in [2] and [3].

This code is a modified and updated version of the code in [4], which in addition to model W1 (in which asymptomatic individuals are infectious to sandflies) now includes model W0, in which only clinical VL cases are infectious. The main file for choosing the model, setting the parameter values and running the simulations is “RunWHOSimltns.m”. The code outputs the results shown in Fig. 2 and Supplementary Figs 1 and 2 in [3], but can also be used to predict the impact of alternative intervention scenarios (different changes in average onset-to-treatment time for clinical cases and indoor residual insecticide spraying coverages).