| Project/Area Number |
20K10447
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 58020:Hygiene and public health-related: including laboratory approach
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| Research Institution | Reitaku University (2023)
The University of Tokyo (2020-2022) |
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Principal Investigator |
Arai Ayumi 麗澤大学, 工学部, 准教授 (10788574)
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| Co-Investigator(Kenkyū-buntansha) |
金杉 洋 東京大学, 空間情報科学研究センター, 協力研究員 (00526907)
ウィタヤンクーン アピチョン 東京大学, 空間情報科学研究センター, 客員研究員 (90726407)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2023: ¥130,000 (Direct Cost: ¥100,000、Indirect Cost: ¥30,000)
Fiscal Year 2022: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | mobile phone data / big data / developing countries / mobility pattern / evidence-based policy / Africa / human mobility / developing country / representativeness / population statistics / malaria / mobility indicator / public health |
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| Outline of Research at the Start |
This study will first address how sociodemographic factors vary with movement, and map demographic variables important to malaria transmission and risk throughout Mozambique during each month of the year.
This study will combine seasonal demography and movement predictions with monthly malaria risk maps to predict populations at-risk of malaria exposure in high transmission months.
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| Outline of Final Research Achievements |
In this study we identified the population mobility patterns that are considered to influence the spread of infectious diseases. By mapping the analysis results, we were able to identify seasonally varying mobility patterns. Linking these to the distribution of malaria cases, enables us to predict risk. This approach makes it possible to identify populations at high risk during peak transmission periods.
The analysis clarified the characteristics of the residential distribution and its changes among highly mobile populations, revealing a tendency for high-risk populations to concentrate in specific times and places. The findings of this analysis can be applicable to other countries in Africa.
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| Academic Significance and Societal Importance of the Research Achievements |
Outcome of our research helps understand seasonally-varying mobility patterns, crucial for advancing intervention in public health domain. It enables predicting disease spread and optimizing healthcare delivery. These insights enable policy makers to tailor interventions and policies effectively.
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