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DOI 10.1016/bs.apar.2014.12.005

Медицинская библиотека · Advances in parasitology · 2015

Modelling lymphatic filariasis transmission and control: modelling frameworks, lessons learned and future directions.

Stolk Wilma A, Stone Chris, de Vlas Sake J

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Аннотация

Mathematical modelling provides a useful tool for policy making and planning in lymphatic filariasis control programmes, by providing trend forecasts based on sound scientific knowledge and principles. This is now especially true, in view of the ambitious target to eliminate lymphatic filariasis as a public health problem globally by the year 2020 and the short remaining timeline to achieve this. To meet this target, elimination programmes need to be accelerated, requiring further optimization of strategies and tailoring to local circumstances. Insights from epidemiological transmission models provide a useful basis. Two general models of lymphatic filariasis transmission and control are nowadays in use to support decision-making, namely a population-based deterministic model (EPIFIL) and an individual-based stochastic model (LYMFASIM). Model predictions confirm that lymphatic filariasis transmission can be interrupted by annual mass drug administration (MDA), but this may need to be continued much longer than the initially suggested 4-6 years in areas with high transmission intensity or poor treatment coverage. However, the models have not been validated against longitudinal data describing the impact of MDA programmes. Some critical issues remain to be incorporated in one or both of the models to make predictions on elimination more realistic, including the possible occurrence of systematic noncompliance, the risk of emerging parasite resistance to anthelmintic drugs, and spatial heterogeneities. Rapid advances are needed to maximize the utility of models in decision-making for the ongoing ambitious lymphatic filariasis elimination programmes.

Для цитирования: Stolk Wilma A, Stone Chris, de Vlas Sake J. Modelling lymphatic filariasis transmission and control: modelling frameworks, lessons learned and future directions.. Advances in parasitology. 2015. DOI: 10.1016/bs.apar.2014.12.005.

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