Publications of Catherine Linard
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See detailMalaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon
Solano Villarreal, Elisa Yoan ULiege; Valdivia, Walter; Pearcy, Morgan et al

in Scientific Reports (2019)

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 ... [more ▼]

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. [less ▲]

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See detailMalaria risk assessment at local level using satellite imagery and BRT in the Peruvian Amazon
Solano-Villarreal, Elisa; Valdivia, Walter; Linard, Catherine ULiege et al

in Archives of Public Health (2018, November 13), 77 (suppl 1)(7),

Background: Malaria in Loreto department remains a public health problem, accounting for more than 90% of reported cases in Peru. This is the first study in the Peruvian Amazon aimed at assessing the risk ... [more ▼]

Background: Malaria in Loreto department remains a public health problem, accounting for more than 90% of reported cases in Peru. This is the first study in the Peruvian Amazon aimed at assessing the risk of malaria transmission using satellite imagery and Boosted Regression Trees (BRT). Methods: Villages with at least one malaria case between 2010 and 2015 from the routine surveillance data in Loreto were georeferenced and their cases aggregated by year and species. Social and environmental variables were derived from Landsat satellite imagery and other spatial data, then included as explanatory variables into a crossvalidated Poisson BRT model for malaria incidence at the local level. Time-dependent explanatory variables included forest coverage (FC, %), annual forest loss (FL,%), cumulative annual rainfall (CAR, mm), annual-mean land surface temperature (LST, oC), normalised difference vegetation index (NDVI), and normalised difference water index (NDWI). Other variables were Euclidean shortest distance to rivers (SDR, meters), time to major populated villages/towns (TPV, minutes), and night-time lights (NTL, mean value 2010-2013) as proxy of population density. BRT accounts for nonlinearities and interactions between factors with high predictive accuracy for disease risk mapping. Results: A total of 1524 villages were included in the analysis (70% of total Loreto’s villages). More than 90% of relative influence in the overall malaria incidence was explained by five variables: NTL (67.8%), TPV (8.1%), FC (6.5%), CAR (5%) and SDR (4.6%). The analysis by species showed a higher influence of environmental variables (CAR, LST, NDVI and NDWI) for P. falciparum (18.4%) than for P. vivax incidence (9.7%). Malaria risk maps were generated based on model predictions taking into account the relative influence of variables. Conclusions: Remotely sensed data analysed using BRT allowed for maps delimiting areas of high malaria risk in Loreto. These maps will help malaria stakeholders to prioritise areas for control interventions. [less ▲]

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