Abstract
Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High-and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Biologie > Department Biologie II |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie |
URN: | urn:nbn:de:bvb:19-epub-34572-1 |
ISSN: | 1827-1987 |
Sprache: | Englisch |
Dokumenten ID: | 34572 |
Datum der Veröffentlichung auf Open Access LMU: | 15. Feb. 2017, 16:04 |
Letzte Änderungen: | 04. Nov. 2020, 13:13 |