Imperial College London
Alternate

Professor Deirdre Hollingsworth

Faculty of Medicine, School of Public Health

Honorary Lecturer
 
 
 
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Contact

 

d.hollingsworth Website

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Smith:2017:10.1016/j.epidem.2017.02.006,
author = {Smith, ME and Singh, BK and Irvine, MA and Stolk, WA and Subramanian, S and Hollingsworth, TD and Michael, E},
doi = {10.1016/j.epidem.2017.02.006},
journal = {Epidemics},
pages = {16--28},
title = {Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework},
url = {http://dx.doi.org/10.1016/j.epidem.2017.02.006},
volume = {18},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Smith,ME
AU  - Singh,BK
AU  - Irvine,MA
AU  - Stolk,WA
AU  - Subramanian,S
AU  - Hollingsworth,TD
AU  - Michael,E
DO  - 10.1016/j.epidem.2017.02.006
EP  - 28
PY  - 2017///
SN  - 1755-4365
SP  - 16
TI  - Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework
T2  - Epidemics
UR  - http://dx.doi.org/10.1016/j.epidem.2017.02.006
UR  - http://hdl.handle.net/10044/1/53348
VL  - 18
ER  -
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