Modelling strategies to break transmission of lymphatic filariasis - aggregation, adherence and vector competence greatly alter elimination

Abstract

BackgroundWith ambitious targets to eliminate lymphatic filariasis over the coming years, there is a need to identify optimal strategies to achieve them in areas with different baseline prevalence and stages of control. Modelling can assist in identifying what data should be collected and what strategies are best for which scenarios.MethodsWe develop a new individual-based, stochastic mathematical model of the transmission of lymphatic filariasis. We validate the model by fitting to a first time point and predicting future timepoints from surveillance data in Kenya and Sri Lanka, which have different vectors and different stages of the control programme. We then simulate different treatment scenarios in low, medium and high transmission settings, comparing once yearly mass drug administration (MDA) with more frequent MDA and higher coverage. We investigate the potential impact that vector control, systematic non-compliance and different levels of aggregation have on the dynamics of transmission and control.ResultsIn all settings, increasing coverage from 65 to 80 % has a similar impact on control to treating twice a year at 65 % coverage, for fewer drug treatments being distributed. Vector control has a large impact, even at moderate levels. The extent of aggregation of parasite loads amongst a small portion of the population, which has been estimated to be highly variable in different settings, can undermine the success of a programme, particularly if high risk sub-communities are not accessing interventions.ConclusionEven moderate levels of vector control have a large impact both on the reduction in prevalence and the maintenance of gains made during MDA, even when parasite loads are highly aggregated, and use of vector control is at moderate levels. For the same prevalence, differences in aggregation and adherence can result in very different dynamics. The novel analysis of a small amount of surveillance data and resulting simulations highlight the need for more individual level data to be analysed to effectively tailor programmes in the drive for elimination.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1152-3) contains supplementary material, which is available to authorized users.

Filarial infection influences mosquito behaviour and fecundity

“…An understanding of LF transmission dynamics is crucial for the implementation and monitoring of elimination programmes91011121314. Mathematical models are being used to guide decision making on the best strategies to eliminate lymphatic filariasis15.…”

“…Mathematical models are being used to guide decision making on the best strategies to eliminate lymphatic filariasis15. Slight changes in vector specific parameters can alter the likelihood of elimination and the most suitable approach to reach transmission breakpoints13. Important parameters of the vectorial capacity equation, such as the host encounter rate, gonotrophic cycle length, host preference and vector death rate, are based on the parasite-naive vector population.…”

Progress towards lymphatic filariasis elimination in Ghana from 2000-2016: analysis of microfilaria prevalence data from 430 communities

“…High risk may occur due to programmatic or demographic factors [12], including migration during treatment period, treatment fatigue, high numbers of middle aged women (child bearing age; majority not taking drug due to pregnancy) etc. Other local factors contributing to transmission may be high biting rate of the mosquitoes and behavior of residents that influence exposure to mosquito bites [32,34]. Moreover, TAS is designed to cover a larger geographical area, with the hope that pockets with residual transmission would be identified in TAS surveys.…”

Measuring and modelling the effects of systematic non-adherence to mass drug administration

“…(2 parameters – coverage and correlation)Scheme by Griffin et al (2010) and Irvine et al (2015)Controlled correlation scheme introduced in this paper…”