EO in Malaria Vector Control and Management 
This project is partially funded by the European Commission Seventh Framework Programme. The project reflects only the author’s views and the Community is not liable for any use that may be made of the information contained herein.
High resolution (HR) EO data was used for generating image base maps, land cover maps, water body maps, distance to water maps, vegetation indices and for the creation of HR population density maps. Very high resolution (VHR) EO data was used for the creation of household maps, which identify round and squared houses, and VHR water body maps. Some of those map products have been used as intermediate input for the malaria incidence and prevalence modelling.​
WELCOME to the website of MALAREO,

The MALAREO project supports the global strategy to have a substantial and sustained reduction in the burden of malaria in the near and mid-term (2015), and the eventual global eradication of malaria in the long term. Geographic Information Systems (GIS), Earth Observation (EO), Global Positioning Systems (GPS) and spatial statistics play a crucial role to plan, apply and monitor optimal malaria vector control measures.

MALAREO wants to promote the use of these techniques by building EO and GIS capacity and
providing relevant EO-based products for the Malaria research and control community. 

MALAREO Map Atlas​​ containing all developed products created to support the daily work of the NMCPs in the project area

All ​​MALAREO products are published on the GeoIQ platform​​

Modeling malaria risk
MALAREO products at a glance

Based on the user requirements of the National Malaria Control Programs (NMCP), which have been surveyed in MALAREO, map products have been identified that are of high relevance for an improved planning of integrated vector control. An EO feasibility study was realized in order to define the required EO input data, the map products and the required methodologies. There are two types of support of these products. First, the direct support of the NMCP; second, the support of epidemiological studies on malaria incidence and prevalence that requires input data from the EO products.
RapidEye satellite imagery (2011, 5m) covering 25.000 km² of South-Africa, Swaziland and Mozambique are classified into malaria-relevant land cover/use classes. These data support MCP's identifying malaria risk areas and targeting malaria vector control measures. Furthermore they are of interest for more advanced epidemiological research looking at the influence of land cover/use on vector presence and malaria risk.
Knowledge about the number and type of housing structure is required for preparing Indoor Residual Spraying (IRS) campaigns. Household maps based on Very High Resolution imagery (0.5 - 1m) fill the gap when GPS-collected terrain data is not available. MALAREO is developing a semi-automatic approach to extract household maps from satellite scenes, fast and accurate.
Water bodies are potential breeding sites for Anopheles Arabiensis, the most prominent vector in the region. Water body maps serve as input for modeling malaria risk and developing breeding sites probability maps. MALAREO is using the 5m resolution RapidEye satellite imagery to map all water bodies in the region. These maps are used in combination with field sampled data to create potential vector breeding sites maps. 
Results from large national malaria surveys are being used to predict malaria prevalence (space/time) and to assess factors (climate, interventions) related to malaria transmission. In MALAREO, EO data from different sensors (MODIS, RapidEye & Meteosat) are being used in combination with Malaria survey data from South-Africa, Swaziland and Mozambique to estimate malaria risk using a Bayesian geostatistical approach.