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Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework
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Smith, Morgan E., Singh, Brajendra K., Irvine, Michael Alastair, Stolk, Wilma A., Subramanian, Swaminathan, Hollingsworth, T. Déirdre and Michael, Edwin (2017) Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework. Epidemics, 18 . pp. 16-28. doi:10.1016/j.epidem.2017.02.006
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WRAP_1-s2.0-S1755436516300615-main.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2523Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.epidem.2017.02.006
Abstract
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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | R Medicine > RC Internal medicine | ||||||||
| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
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| Library of Congress Subject Headings (LCSH): | Elephantiasis -- Prevention -- Mathematical models -- Africa, Elephantiasis -- Prevention -- Mathematical models -- Southeast Asia, Elephantiasis -- Prevention -- Mathematical models -- Papua New Guinea | ||||||||
| Journal or Publication Title: | Epidemics | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 1755-4365 | ||||||||
| Official Date: | March 2017 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 17 March 2017 | ||||||||
| Volume: | 18 | ||||||||
| Page Range: | pp. 16-28 | ||||||||
| DOI: | 10.1016/j.epidem.2017.02.006 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Bill & Melinda Gates Foundation, University of Notre Dame. Eck Institute for Global Health | ||||||||
| Adapted As: |
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