Cholera prediction models using satellite remote sensing
| Project/Area Number |
26305026
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 海外学術 |
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| Research Field |
Hygiene and public health
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| Research Institution | Nagasaki University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
森山 雅雄 長崎大学, 工学研究科, 准教授 (00240911)
一瀬 休生 長崎大学, 熱帯医学研究所, 教授 (70176296)
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| Research Collaborator |
YAMAMOTO Kazuhide 宇宙航空研究開発機構
Wellington Otieno マセノ大学
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
Fiscal Year 2016: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2014: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
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| Keywords | 疫学 / リモートセンシング / 感染症 / 気候変動 / 統計解析 / 時系列解析 / 衛星観測 / ケニア |
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| Outline of Final Research Achievements |
This study aimed to develop a diarrhoea prediction model using environmental information extracted from satellite remote sensing. We developed a time-series regression model by applying a distributed lag non-linear model to concurrently describe the non-linear relationship between environmental factors and the number of diarrhoea cases over multiple weeks of lag. The number of weekly diarrhoea cases visiting hospitals around the Lake Victoria in Nyanza province, Kenya was revealed to be predictable using environmental data including land surface temperature, water surface temperature, normalized difference vegetation index (NDVI) around the hospital, area of high NDVI on the lake surface (as an index of floating water hyacinth) and rainfall that were extracted from the MODIS satellite images and the GSMap.
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Report
(5 results)
Research Products
(7 results)
