Imperial College London
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DrPierreNouvellet

Faculty of Medicine, School of Public Health

Visiting Reader
 
 
 
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p.nouvellet

 
 
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UG 11Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ledien:2022:10.1371/journal.pntd.0010594,
author = {Ledien, J and Cucunuba, ZM and Parra-Henao, G and Rodriguez-Mongui, E and Dobson, AP and Adamo, SB and Basanez, M-G and Nouvellet, P},
doi = {10.1371/journal.pntd.0010594},
journal = {PLoS Neglected Tropical Diseases},
pages = {1--19},
title = {Linear and machine learning modelling for spatiotemporal disease predictions: force-of-infection of chagas disease},
url = {http://dx.doi.org/10.1371/journal.pntd.0010594},
volume = {16},
year = {2022}
}
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TY  - JOUR
AB  - BackgroundChagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.Methodology/principal findingsWe compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty.Conclusions/significanceThe choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to o
AU  - Ledien,J
AU  - Cucunuba,ZM
AU  - Parra-Henao,G
AU  - Rodriguez-Mongui,E
AU  - Dobson,AP
AU  - Adamo,SB
AU  - Basanez,M-G
AU  - Nouvellet,P
DO  - 10.1371/journal.pntd.0010594
EP  - 19
PY  - 2022///
SN  - 1935-2727
SP  - 1
TI  - Linear and machine learning modelling for spatiotemporal disease predictions: force-of-infection of chagas disease
T2  - PLoS Neglected Tropical Diseases
UR  - http://dx.doi.org/10.1371/journal.pntd.0010594
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000922468300038&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010594
UR  - http://hdl.handle.net/10044/1/106155
VL  - 16
ER  -
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