Spatiotemporal Modeling of Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana.

Rift Valley fever dynamics in Senegal: a project for pro-active adaptation and improvement of livestock raising management.

The multi-disciplinary French project "Adaptation à la Fiévre de la Vallée du Rift" (AdaptFVR) has concluded a 10-year constructive interaction between many scientists/partners involved with the Rift Valley fever (RVF) dynamics in Senegal. The three targeted objectives reached were (i) to produce--in near real-time--validated risk maps for parked livestock exposed to RVF mosquitoes/vectors bites; (ii) to assess the impacts on RVF vectors from climate variability at different time-scales including climate change; and (iii) to isolate processes improving local livestock management and animal health. Based on these results, concrete, pro-active adaptive actions were taken on site, which led to the establishment of a RVF early warning system (RVFews).

Risk mapping of Anopheles gambiae s.l. densities using remotely-sensed environmental and meteorological data in an urban area: Dakar, Senegal.

Introduction: High malaria transmission heterogeneity in an urban environment is basically due to the complex distribution of Anopheles larval habitats, sources of vectors. Understanding 1) the meteorological and ecological factors associated with differential larvae spatio-temporal distribution and 2) the vectors dynamic, both may lead to improving malaria control measures with remote sensing and high resolution data as key components. In this study a robust operational methodology for entomological malaria predictive risk maps in urban settings is developed.

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

Introduction: The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping.

Methods: A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands.

Conditions of malaria transmission in Dakar from 2007 to 2010.

Background: Previous studies in Dakar have highlighted the spatial and temporal heterogeneity of Anopheles gambiae s.l. biting rates.