Transmission models and management of lymphatic filariasis elimination.
The planning and evaluation of parasitic control programmes are complicated by the many interacting population dynamic and programmatic factors that determine infection trends under different control options. A key need is quantification about the status of the parasite system state at any one given timepoint and the dynamic change brought upon that state as an intervention program proceeds. Here, we focus on the control and elimination of the vector-borne disease, lymphatic filariasis, to show how mathematical models of parasite transmission can provide a quantitative framework for aiding the design of parasite elimination and monitoring programs by their ability to support (1) conducting rational analysis and definition of endpoints for different programmatic aims or objectives, including transmission endpoints for disease elimination, (2) undertaking strategic analysis to aid the optimal design of intervention programs to meet set endpoints under different endemic settings and (3) providing support for performing informed evaluations of ongoing programs, including aiding the formation of timely adaptive management strategies to correct for any observed deficiencies in program effectiveness. The results also highlight how the use of a model-based framework will be critical to addressing the impacts of ecological complexities, heterogeneities and uncertainties on effective parasite management and thereby guiding the development of strategies to resolve and overcome such real-world complexities. In particular, we underscore how this approach can provide a link between ecological science and policy by revealing novel tools and measures to appraise and enhance the biological controllability or eradicability of parasitic diseases. We conclude by emphasizing an urgent need to develop and apply flexible adaptive management frameworks informed by mathematical models that are based on learning and reducing uncertainty using monitoring data, apply phased or sequential decision-making to address extant uncertainty and focus on developing ecologically resilient management strategies, in ongoing efforts to control or eliminate filariasis and other parasitic diseases in resource-poor communities.
Michael
Edwin
E
Department of Infectious Disease Epidemiology, Faculty of Medicine, St. Mary's campus, Imperial College London, Norfolk Place, London W2 1PG, UK. e.michael@imperial.ac.uk
Gambhir
Manoj
M
D000818:Animals; D003140:Communicable Disease Control / Q000379:methods; D009033:Culicidae / Q000469:parasitology; D004605:Elephantiasis, Filarial / Q000469:parasitology / Q000517:prevention & control* / Q000635:transmission*; D006790:Host-Parasite Interactions; D006801:Humans; D007303:Insect Vectors / Q000469:parasitology; D008954:Models, Biological