Managing malaria from above According to the World Health Organization, out of the hundreds of millions of malaria cases, over 80 % are located in Africa. A European–African consortium has worked on the development of Earth Observation (EO) systems for controlling malaria-transmitting mosquitoes. Health © Thinkstock There is a global front towards reducing the burden of malaria by 2015. For effective implementation of malaria vector control measures, stakeholders are considering EO, geographical information and global positioning systems (GIS and GPS, respectively). The EU-funded project 'EO in malaria vector control and management' aimed to develop such technology and introduce it into malaria control programmes The area of interest was the cross-border region of Mozambique, South Africa and Swaziland. These three countries have different malaria epidemiological profiles. Swaziland is currently in the malaria elimination stage while South Africa is in malaria pre-elimination stage and Mozambique is in malaria control stage. The project partners developed EO applications to support the daily work of the National Malaria Control Programs (NMCP). The solutions generated by MALAREO mainly included a variety of thematic maps, e.g. land use/land cover map, water body map, household map. High-resolution maps covering the whole project area were generated to assist malaria programme managers in the planning of optimal malaria vector control measures. EO data from different sensors (MODIS, ASTER, RAPIDEYE & METEOSAT) are being used in combination with malaria survey data from South Africa, Swaziland and Mozambique. RapidEye data was used to generate malaria-relevant land cover/use classes which are of special interest for NMCP’s for identifying malaria risk areas and looking at the influence of land cover/use on vector presence and malaria risk. Very high-resolution imagery was used to identify houses and small huts. Information on the house type, amount and distribution is very valuable for a more efficient indoor residual spraying (IRS). As mosquitoes are drawn to water for breeding, water bodies are potential breeding sites for the most prominent malaria vector in the region. Water body maps serve as input for modelling malaria risk and developing breeding sites probability maps. Low resolution EO data (rainfall estimates, land surface temperature, vegetation indices) were used to assess factors related to malaria transmission. By taking into account the population distribution in the project area, partners were able to model the spatial distribution and spread of malaria. All these EO products were combined into a MALAREO MapBook for assisting in the implementation of malaria vector control measures. With appropriate stakeholder training, EO solutions could be applied in the fight against malaria.
