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Mapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique

2018-04-19Artigo científico Acesso aberto
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dc.contributor.authorFerrão, João L.
dc.contributor.authorNiquisse, Sérgio
dc.contributor.authorMendes, Jorge M.
dc.contributor.authorPainho, Marco
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2018-05-07T22:15:23Z
dc.date.available2018-05-07T22:15:23Z
dc.date.issued2018-04-19
dc.descriptionFerrão, J. L., Niquisse, S., Mendes, J. M., & Painho, M. (2018). Mapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. International Journal of Environmental Research and Public Health, 15(4), [795]. DOI: 10.3390/ijerph15040795
dc.description.abstractBackground: 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 1º 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13412475
dc.identifier.doi10.3390/ijerph15040795
dc.identifier.issn1661-7827
dc.identifier.otherPURE: 4110256
dc.identifier.otherPURE UUID: 4e81e711-8a3f-4cce-b419-596e96c9d24b
dc.identifier.otherScopus: 85045927404
dc.identifier.otherWOS: 000434868800234
dc.identifier.otherORCID: /0000-0003-1136-3387/work/151384392
dc.identifier.otherORCID: /0000-0003-2251-3803/work/152085019
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85045927404&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85045927404
dc.language.isoeng
dc.peerreviewedyes
dc.subjectMalaria risk
dc.subjectMapping
dc.subjectModelling
dc.subjectPublic precision health
dc.subjectPublic Health, Environmental and Occupational Health
dc.subjectHealth, Toxicology and Mutagenesis
dc.subjectSDG 3 - Good Health and Well-being
dc.subjectSDG 13 - Climate Action
dc.subjectSDG 15 - Life on Land
dc.titleMapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambiqueen
dc.typejournal article
degois.publication.issue4
degois.publication.titleInternational Journal of Environmental Research and Public Health
degois.publication.volume15
dspace.entity.typePublication
rcaap.rightsopenAccess

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