TY - JOUR
T1 - Modelling Lymphatic Filariasis Transmission and Control
T2 - Modelling Frameworks, Lessons Learned and Future Directions
AU - Stolk, Wilma A.
AU - Stone, Chris
AU - de Vlas, Sake J.
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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 4-6years 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.
AB - 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 4-6years 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.
KW - Control
KW - Elimination
KW - Lymphatic filariasis
KW - Mathematical modelling
KW - Prediction
KW - Review
UR - http://www.scopus.com/inward/record.url?scp=84924515137&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924515137&partnerID=8YFLogxK
U2 - 10.1016/bs.apar.2014.12.005
DO - 10.1016/bs.apar.2014.12.005
M3 - Article
C2 - 25765197
AN - SCOPUS:84924515137
SN - 0065-308X
VL - 87
SP - 249
EP - 291
JO - Advances in Parasitology
JF - Advances in Parasitology
ER -