Name Analyzing Malaria with Satellite Data
Source UN Women
Hyperlink to Source
Description A study creating high spatiotemporal resolution maps of malaria risk, focusing on urban and peri-urban areas and predicting maternal morbidity due to malaria In a given environmental, economic, cultural, and health service context. Remote sensing satellite data on vegetation density, soil moisture, population density, and spatial pattern of human infrastructure have long been used to predict levels of malaria risk. Advances in computing allow more powerful use of these big datasets, including analysis of extreme spatial and temporal heterogeneity and inclusion of greater numbers of explanatory variables. This project seeks to create malaria risk maps for the Amazon Basin, focusing first on urban and peri-urban zones of the Brazil/Guyana border, areas with highly variable vector habitat and elevated illness incidence. At least two vector distribution mapping studies in this region exist, but to our knowledge there is no high resolution dynamic mapping of malaria risk. The first phase of the project will use remote sensing data and existing health records, in combination with information about the economic, cultural, and health system context, to estimate a spatial regression model predicting morbidity burden among pregnant women, using DALYs as the principal metric. The second phase will then test the accuracy of this model using data collected in real-time.
Long description
Lead Institution UN Women
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Partners Getlio Vargas Foundation, Amazon Malaria Initiative
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Type Knowledge
Potential for reuse Low
Open source No
Open Source URL
Data Input Formats
Data Output Formats
Maintenance Requirements
Plug & Play No
Outputs compatible with indicators from Official Statistics No
Status Concept
Country use cases
Target Groups
Statistical Institute
Other Government Agencies
Civil Society Organizations
Sectors where the innovation can be applied
  • Gender
  • ,
  • Health
  • Areas of technological innovation
  • Visual Analytics
  • Areas of management practice innovations
    Use of Alternative Data Sources
    Areas of institutional process innovations
    Framework
    Case Study URL with Public Sector Participation
    Case Study Description
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    Created On 2014-04-12
    Updated On 2014-04-12
    Status Published
    Framework
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