Publication: Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period
Issued Date
2020-08-01
Resource Type
ISSN
14761645
Other identifier(s)
2-s2.0-85089203133
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Mahidol University
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SCOPUS
Bibliographic Citation
The American journal of tropical medicine and hygiene. Vol.103, No.2 (2020), 793-809
Suggested Citation
Xiaotao Zhao, Weerapong Thanapongtharm, Siam Lawawirojwong, Chun Wei, Yerong Tang, Yaowu Zhou, Xiaodong Sun, Liwang Cui, Jetsumon Sattabongkot, Jaranit Kaewkungwal Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period. The American journal of tropical medicine and hygiene. Vol.103, No.2 (2020), 793-809. doi:10.4269/ajtmh.19-0854 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/57944
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Title
Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period
Abstract
In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance-response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China-Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.