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Background: Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods: A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results: Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions: The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

The incorporation of ecosystem services (ES) information in planning decisions is an important factor in the sustainable use of natural resources. However, studies assessing the levels and changes of these services at national level are rare. In this paper, we estimate past and future changes in multiple ES and biodiversity, as a consequence of land cover change (LCC) in Mozambique. Water yield, water quality, erosion regulation, climate regulation, and biodiversity were modeled using a spatially explicit approach. Changes in ES and biodiversity were mapped at province level between 2005 and 2009. Through the use of a land change model, land cover was projected for 2025, and the resulting impacts on ES and biodiversity were estimated. We found a moderate increase in climate regulating service between 2005 and 2009. However, water quality (nutrient retention) and biodiversity decreased. LCC for 2025 is expected to have a similar impact on these ES. This study contributes with a methodology that can be useful for monitoring ES and assist decision policies affecting ES provision and trade-offs. ARTICLE HISTORY

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