Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework.

Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG). We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders.

Smith ME ，Singh BK ，Irvine MA ，Stolk WA ，Subramanian S ，Hollingsworth TD ，Michael E ... -  《-》

Heterogeneous dynamics, robustness/fragility trade-offs, and the eradication of the macroparasitic disease, lymphatic filariasis.

Michael E ，Singh BK 《BMC Medicine》

Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis.

Singh BK ，Michael E 《Parasites & Vectors》

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination.

Michael E ，Sharma S ，Smith ME ，Touloupou P ，Giardina F ，Prada JM ，Stolk WA ，Hollingsworth D ，de Vlas SJ ... -  《PLoS Neglected Tropical Diseases》

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.

Michael E ，Singh BK ，Mayala BK ，Smith ME ，Hampton S ，Nabrzyski J ... -  《BMC Medicine》