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1.
Rev. moçamb. ciênc. saúde ; 6(1): 9-14, Out. 2020. tab, map
Artículo en Portugués | AIM (África), RSDM | ID: biblio-1380981

RESUMEN

Objectivo: Mapear o potencial risco de transmissão do novo coronavírus em Moçambique de modo a identificar os distritos cujas características sociodemográficas favorecem a propagação do vírus. Métodos: Usou-se a modelação espacial para determinar o risco relativo de propagação da COVID-19 num distrito em relação ao outro com base nos seguintes factores sociodemográficos: densidade populacional, tamanho médio de agregado familiar, percentagem da população jovem de 15-34 anos e percentagem da população que vive num raio de 2 km de uma estrada classificada. Primeiro, para cada factor foi estimado um risco relativo dividindo os distritos em quintis, e, em segundo lugar, os riscos individuais de cada factor foram somados com igual peso para estimar o risco agregado de transmissão da COVID-19 por distrito. Resultados: Dezanove distritos localizados sobretudo nos principais centros urbanos e no corredor da Beira apresentam alto risco de propagação da COVID-19 em função das suas características sociodemográficas; 24 distritos mostram risco médio-alto e distribuem-se pelas regiões centro e sul do país; 60 distritos localizados nas regiões centro e sul e no interior da região norte apresentam risco médio e; 58 distritos mostram risco médio-baixo ou baixo de transmissão da COVID-19 e encontram-se no litoral centro-norte do país. Conclusão: Os distritos cujo perfil sociodemográfico é favorável à rápida propagação do novo coronavírus são os das grandes cidades e os localizados nas principais rotas de transporte. No entanto, este padrão de risco é susceptível de alterações em função da celeridade, abrangência e níveis de observância das medidas de prevenção e/ou de mitigação da COVID-19. Assim, recomenda-se que as medidas de prevenção e mitigação tenham em conta o risco potencial em cada distrito em função das suas características sociodemográficas.


Objective: To map the potential risk of COVID-19 transmission in Mozambique in order to identify districts with sociodemographic characteristics that favour the spread of coronavirus. Methods: Spatial modelling was used to determine the relative risk of COVID-19 transmission in a certain district in relation to other districts based on the following sociodemographic factors: population density, mean number of household members, the percentage of the young population aged 15-34 and the proportion of a district's population living within two kilometres of a classified road. First, a relative risk due to each factor was estimated grouping the districts into quintiles and, second, the individual risks were added with equal weight to estimate the aggregate relative risk of COVID-19 transmission per district. Results: Nineteen districts located in the main urban centres and along the Beira corridor were found to be at a high relative risk of COVID-19 transmission; 24 districts located mainly in central and southern regions display a medium-high risk category; 60 districts located in the central and southern regions and in the hinterland of the northern region show a medium risk category and; 58 districts exhibit a medium-low or low risk category of COVID-19 transmission and are mainly located at the eastern part of the central-north region. Conclusion: The districts with sociodemographic profile favouring the spread of the new coronavirus are those in the big cities and those located along the main transportation routes. However, the pattern of risk is subject to changes due to the speed, coverage and level of compliance with COVID-19 prevention and mitigation measures. It is recommended that COVID-19 prevention and mitigation measures should take into account the potential risk of each district.


Asunto(s)
Humanos , Masculino , Adolescente , Riesgo , Coronavirus/inmunología , COVID-19/diagnóstico , Virus , Transmisión de Enfermedad Infecciosa , Transmisión de Enfermedad Infecciosa/prevención & control , Foraminíferos/crecimiento & desarrollo , Factores Sociodemográficos , Mitigación de Desastres , Cristaluria , Mozambique
2.
Artículo en Inglés | MEDLINE | ID: mdl-29671756

RESUMEN

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.


Asunto(s)
Clima , Malaria/epidemiología , Factores Socioeconómicos , Análisis Espacial , Altitud , Sistemas de Información Geográfica , Mapeo Geográfico , Humanos , Incidencia , Mozambique/epidemiología , Prevalencia , Salud Pública , Medición de Riesgo/métodos , Factores de Riesgo , Temperatura
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