ArticleScientific reports2019
Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Malaria, relationship with climatic variables and deforestation in Colombia, Latin America and the Caribbean from 2000 to 2020: a systematic review.Malaria journal · 2024Pooled it
- Spatial patterns and risk mapping of opisthorchiasis and soil-transmitted helminth infections in Thailand using Bayesian geostatistical models.Scientific reports · 2026Article
- The resurgence of malaria in Northern Iran in 2023: a wake-up call.BMC infectious diseases · 2025Article
- Knowledge, attitudes, and practices regarding malaria transmission and prevention in an indigenous Maijuna community: a qualitative study in the Peruvian Amazon.Malaria journal · 2024Article
- Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.The American journal of tropical medicine and hygiene · 2024Article
- Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees.bioRxiv : the preprint server for biology · 2023Article
- Article
- Natural Infection of Nyssorhynchus darlingi and Nyssorhynchus benarrochi B with Plasmodium during the Dry Season in the Understudied Low-Transmission Setting of Datem del Marañon Province, Amazonian Peru.The American journal of tropical medicine and hygiene · 2023Article
- Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda.Malaria journal · 2023Article
- The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products.Science China. Life sciences · 2023Article
- Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt.Infectious diseases of poverty · 2021Article
- Remote sensing of environmental risk factors for malaria in different geographic contexts.International journal of health geographics · 2021Article
- Integrating Parasitological and Entomological Observations to Understand Malaria Transmission in Riverine Villages in the Peruvian Amazon.The Journal of infectious diseases · 2021Article
- Temporal and Microspatial Heterogeneity in Transmission Dynamics of Coendemic Plasmodium vivax and Plasmodium falciparum in Two Rural Cohort Populations in the Peruvian Amazon.The Journal of infectious diseases · 2021Article
- Progress towards onchocerciasis elimination in Côte d'Ivoire: A geospatial modelling study.PLoS neglected tropical diseases · 2021Article
- Updates on malaria epidemiology and profile in Cabo Verde from 2010 to 2019: the goal of elimination.Malaria journal · 2020Article
- Vector-Focused Approaches to Curb Malaria Transmission in the Brazilian Amazon: An Overview of Current and Future Challenges and Strategies.Tropical medicine and infectious disease · 2020Review
- Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period.The American journal of tropical medicine and hygiene · 2020Article
- Economic costs analysis of uncomplicated malaria case management in the Peruvian Amazon.Malaria journal · 2020Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 very-high-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.
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