ArticlePloS one2016
Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Fine-Scale Risk Mapping for Dengue Vector Using Spatial Downscaling in Intra-Urban Areas of Guangzhou, China.Insects · 2025Article
- Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India.PloS one · 2025Article
- Understanding the impact of mobility on Plasmodium spp. carriage in an Amazon cross-border area with low transmission rate.PLOS global public health · 2024Article
- The influence of the environment and indoor residual spraying on malaria risk in a cohort of children in Uganda.Scientific reports · 2022Article
- Spatio-Temporal Dynamics ofInternational journal of environmental research and public health · 2021Article
- Resurgence risk for malaria, and the characterization of a recent outbreak in an Amazonian border area between French Guiana and Brazil.BMC infectious diseases · 2020Article
- Article
- Prevalence ofThe American journal of tropical medicine and hygiene · 2020Article
- Identification of French Guiana anopheline mosquitoes by MALDI-TOF MS profiling using protein signatures from two body parts.PloS one · 2020Article
- Vector competence, vectorial capacity of Nyssorhynchus darlingi and the basic reproduction number of Plasmodium vivax in agricultural settlements in the Amazonian Region of Brazil.Malaria journal · 2019Article
- Minimal genetic differentiation of the malaria vector Nyssorhynchus darlingi associated with forest cover level in Amazonian Brazil.PloS one · 2019Article
- Contribution of the French army health service in support of expertise and research in infectiology in Africa.New microbes and new infections · 2018Article
- Modeling Spatio-temporal Malaria Risk Using Remote Sensing and Environmental Factors.Iranian journal of public health · 2018Article
- Applications of Space Technologies to Global Health: Scoping Review.Journal of medical Internet research · 2018Article
- Abundance of impacted forest patches less than 5 kmScientific reports · 2018Article
- Mapping multiple components of malaria risk for improved targeting of elimination interventions.Malaria journal · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l'Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l'Oyapock. The final cross-validated model integrated two landscape variables-dense forest surface and built surface-together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.