RESUMEN
For modelling the spatial distribution of malaria incidence, accurate and detailed information on population size and distribution are of significant importance. Different, global, spatial, standard datasets of population distribution have been developed and are widely used. However, most of them are not up-to-date and the low spatial resolution of the input census data has limitations for contemporary, national- scale analyses. The AfriPop project, launched in July 2009, was initiated with the aim of producing detailed, contemporary and easily updatable population distribution datasets for the whole of Africa. High-resolution satellite sensors can help to further improve this dataset through the generation of high-resolution settlement layers at greater spatial details. In the present study, the settlement extents included in the MALAREO land use classification were used to generate an enhanced and updated version of the AfriPop dataset for the study area covering southern Mozambique, eastern Swaziland and the malarious part of KwaZulu-Natal in South Africa. Results show that it is possible to easily produce a detailed and updated population distribution dataset applying the AfriPop modelling approach with the use of high-resolution settlement layers and population growth rates. The 2007 and 2011 population datasets are freely available as a product of the MALAREO project and can be downloaded from the project website.
Asunto(s)
Bases de Datos Factuales , Malaria/epidemiología , Densidad de Población , Imágenes Satelitales , Esuatini/epidemiología , Mapeo Geográfico , Humanos , Incidencia , Mozambique/epidemiología , Sudáfrica/epidemiologíaRESUMEN
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.