Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data

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.

Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example—Application to the development of an operational mapping tool of vector populations

“…Two main approaches are used to understand and predict mosquito population dynamics: i) process-based (or mechanistic) models describing biological knowledge within a mathematical or computational framework, and ii) empirical (or statistical) models, which try to find, from the observed data, a predictive function of the response variable (mosquito populations) based on a set of predictors within a statistical or a machine learning framework. Both approaches have been successfully applied to different mosquito species and geographical contexts [5][6][7][8][9][10][11][12][13][14][15][16][17], resulting in a better understanding of their distribution [5-8, 11, 12, 16] and dynamics [9,10,13,17,18] and the assessment of different mosquito control strategies [19,20]. However, most case studies only develop one of the two approaches (either empirical [5-8, 11, 12, 14, 16] or process-based [9,10,13,15,17] depending on the availability of data and knowledge), and do not compare the capacity of the two approaches to predict mosquito population dynamics.…”

Resurgence risk for malaria, and the characterization of a recent outbreak in an Amazonian border area between French Guiana and Brazil

“…In the study region, temperatures are usually not considered as a good predictor of malaria vector presence and density as it oscillates in the ideal range of values for the mosquito development all the time, and present diurnal variations that exceed the seasonal ones [29]. However, past studies demonstrated significant positive correlations between precipitations and Anopheles darlingi abundance [12,30]. Thus, we can expect significant relationships between precipitations and malaria cases.…”

“…Present and past studies show the influence of vector dynamics on malaria transmission [12,28]. In 2017, the heaviest rainfall hit the area since 2000; it may have had an impact on the productivity of vector breeding sites in the transitional period between the wet and the dry season, and the abundance of An.…”

“…This is particularly important in border areas, which complicate effective implementation of malaria control interventions [2,10]. Anopheles darlingi is the predominant malaria vector species and in this region [11,12].…”

Identification of French Guiana anopheline mosquitoes by MALDI-TOF MS profiling using protein signatures from two body parts

“…Anopheles adult female mosquitoes were selected from field mosquito collections done in 6 distinct sites from French Guiana, during entomological surveys, using different collection methods, over different sampling periods (Figure 1) [3,39–41]. After collections, mosquitoes were sorted by genera and Anopheles mosquitoes were morphologically identified under a binocular loupe at a magnification of ×56 (Leica M80, Leica, Nanterre, France) using standard taxonomic keys for the region (Floch and Abonnenc 1951, Forattini 1962, Faran and Linthicum 1981).…”