Article (Scientific journals)
Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon
Solano Villarreal, Elisa Yoan; Valdivia, Walter; Pearcy, Morgan et al.
2019In Scientific Reports
Peer Reviewed verified by ORBi
 

Files


Full Text
s41598-019-51564-4.pdf
Publisher postprint (4.69 MB)
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
malaria; risk assessment; Peru; Maping; satellite imagery; boosted regression tree
Abstract :
[en] This is the first study to assess the risk of co-endemic Plasmodium vivax and Plasmodium falciparum transmission in the Peruvian Amazon using boosted regression tree (BRT) models based on social and environmental predictors derived from satellite imagery and data. Yearly cross-validated BRT models were created to discriminate high-risk (annual parasite index API > 10 cases/1000 people) and veryhigh-risk for malaria (API > 50 cases/1000 people) in 2766 georeferenced villages of Loreto department, between 2010–2017 as other parts in the article (graphs, tables, and texts). Predictors were cumulative annual rainfall, forest coverage, annual forest loss, annual mean land surface temperature, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), shortest distance to rivers, time to populated villages, and population density. BRT models built with predictor data of a given year efficiently discriminated the malaria risk for that year in villages (area under the ROC curve (AUC) > 0.80), and most models also effectively predicted malaria risk in the following year. Cumulative rainfall, population density and time to populated villages were consistently the top three predictors for both P. vivax and P. falciparum incidence. Maps created using the BRT models characterize the spatial distribution of the malaria incidence in Loreto and should contribute to malaria-related decision making in the area.
Disciplines :
Public health, health care sciences & services
Immunology & infectious disease
Author, co-author :
Solano Villarreal, Elisa Yoan ;  Université de Liège - ULiège > Doct. sc. bioméd. & pharma. (paysage)
Valdivia, Walter
Pearcy, Morgan
Linard, Catherine ;  Université de Namur - UNamur
Pasapera-Gonzales, José
Moreno-Guttierrez, Diamantina
Lejeune, Philippe  ;  Université de Liège - ULiège > Département GxABT > Gestion des ressources forestières et des milieux naturels
Llanos-Cuentas, Alejandro
Speybroeck, Niko
Hayette, Marie-Pierre  ;  Université de Liège - ULiège > Département des sciences biomédicales et précliniques > Bactériologie, mycologie, parasitologie, virologie
Rosas-Aguirre, Angel
Language :
English
Title :
Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon
Alternative titles :
[en] Evaluation du risque malarique avec utilisation de l'imagerie satellite et des arbres de regression "boostés" dans la région de l'Amazone au Pérou
Publication date :
October 2019
Journal title :
Scientific Reports
eISSN :
2045-2322
Publisher :
Nature Publishing Group, London, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
PRD
Funders :
ARES - Académie de Recherche et d'Enseignement Supérieur
Available on ORBi :
since 23 November 2019

Statistics


Number of views
119 (10 by ULiège)
Number of downloads
85 (2 by ULiège)

Scopus citations®
 
26
Scopus citations®
without self-citations
23
OpenCitations
 
17
OpenAlex citations
 
38

Bibliography


Similar publications



Contact ORBi