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


METHODS
We 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.


RESULTS
In 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.


CONCLUSIONS
Even 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.
School of Life Sciences, University of Warwick, Gibbet Hill Road, CV4 7AL, Coventry, UK. M.Irvine.1@Warwick.ac.uk.
M A
MA
Irvine
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
L J
LJ
Reimer
Kenya Medical Research Institute (KEMRI), P.O. Box 54840, 00200, Nairobi, Kenya.
S M
SM
Njenga
Faculty of Medicine, University of Colombo, Colombo, Sri Lanka.
S
S
Gunawardena
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
L
L
Kelly-Hope
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
M
M
Bockarie
School of Life Sciences, University of Warwick, Gibbet Hill Road, CV4 7AL, Coventry, UK.|Mathematics Institute, University of Warwick, Gibbet Hill Road, CV4 7AL, Coventry, UK.
T D
TD
Hollingsworth
D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D004605:Elephantiasis, Filarial / Q000188:drug therapy* / Q000453:epidemiology / Q000635:transmission*; D005369:Filaricides / Q000008:administration & dosage*; D007300:Insect Control / Q000379:methods*; D007630:Kenya / Q000453:epidemiology; D008962:Models, Theoretical; D015995:Prevalence; D013188:Sri Lanka / Q000453:epidemiology
D005369:Filaricides