Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique
| Autor: | João Ferrão, Alberto Tungaza, Dominique Earland, Kelly M. Searle, Roberto Mendes, Marcos F. Ballat, Anisio Novela |
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| Rok vydání: | 2021 |
| Předmět: |
Rural Population
Health Toxicology and Mutagenesis 030231 tropical medicine malaria lcsh:Medicine Manica Article Africa Southern law.invention 03 medical and health sciences 0302 clinical medicine Malaria transmission law parasitic diseases medicine Humans Malaria risk 030212 general & internal medicine mapping Socioeconomics Mozambique risk Sussundenga village biology Incidence Incidence (epidemiology) lcsh:R Public Health Environmental and Occupational Health biology.organism_classification medicine.disease Geography Transmission (mechanics) Malaria control Malaria |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 18 Issue 5 International Journal of Environmental Research and Public Health, Vol 18, Iss 2568, p 2568 (2021) Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes ISBN: 9789392117381 |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph18052568 |
| Popis: | Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions. |
| Databáze: | OpenAIRE |
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