Mapping seasonal demography and mobility for malaria elimination
| 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 | The University of Tokyo |
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Principal Investigator |
新井 亜弓 東京大学, 空間情報科学研究センター, 特任助教 (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 |
Granted (Fiscal Year 2021)
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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 / human mobility / developing country / 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 Annual Research Achievements |
Work package I: Linking seasonal demography and mobility patterns
- Examined seasonal landscape through changes in population distribution: We examined changes in the distribution of residential locations across different seasons at administrative levels. We found the magnitude of seasonal changes are not significant when the population employed for the analysis are the whole population; it is most probably because the share of highly-mobile people is relatively small and majority of them remain stationary at the administrative levels. Because of this, we then evaluated seasonal characteristics only for the highly-mobile population and the remainder separately. Then, combined these result by region for mapping seasonal geographical landscape.
- Developed methodologies for filtering the highly-mobile population: Given that the population who have high possibilities of being the carrier of transmission are mobile, analysis plan was changed to focusing more on changes in seasonal landscape for the mobile population. We compute the radius of gyration and center of gyration to see whether their residential locations at the district level changed. We tested several thresholds to extract the highly-mobile population and examined how changing the thresholds affects the results.
Work package II: Focusing on high transmission seasons and extending to other countries
- We are considering the application of this analysis in one of western African countries where we are conducting the analysis on internal migration.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We initially planned to estimate the demographic attribute of mobile phone users from phone usage activities. In this fiscal year it was changed to evaluating the user characteristics based on the mobility attributes, which are closely related with the disease transmission.
We are now utilizing mobility indicators used for understanding changes in mobility patterns during the COVID-19 for our other studies. We are developing the analysis model based on the existing analytical framework, which helped us to get back on track.
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| Strategy for Future Research Activity |
We plan conduct the following analysis to characterize the attributes of highly-mobile population, which will be used for describing the changes in seasonal population landscape. We also examine how we could generalize our model to one of west African countries.
1) Perform the analysis on changes in distribution and mobility patterns of the highly-mobile population.
2) Examine their geographical concentration and exodus, which will vary seasonally. By using the result we identify specific areas and timing where high-risk population start to concentrate.
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Report
(2 results)
Research Products
(5 results)
