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Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery
모잠비크 국외연구자료 기타 Federica Giardina, Jonas Franke, Penelope Vounatsou Geospatial Health 발간일 : 2016-10-30 등록일 : 2016-10-30 원문링크
The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union’s 7<sup>th</sup> Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the <em>enhanced</em> population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature. However, the presence of wetlands and bare soil are also very important factors. The model validation performed on a subset of locations revealed that the use of high-resolution covariates (MALAREO land cover and elevation data) improved prediction performance.
본 페이지에 등재된 자료는 운영기관(KIEP) 및 AIF의 공식적인 입장을 대변하고 있지 않습니다.
| 이전글 | GLOBAL ECONOMIC CRISIS: A VIEW FROM SOUTH AFRICA | 2016-10-30 |
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| 다음글 | DETERMINANTS OF ON-FARM DIVERSIFICATION AMONG RURAL HOUSEHOLDS: EMPIRICAL EVIDEN... | 2016-10-30 |