Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 103-116). / Dengue fever is currently the fastest spreading mosquito-borne disease in the world, and a substantial public health problem due to its geographic spread, intensity, and resulting economic impact. The disease is endemic in Singapore, and has caused multiple outbreaks of unprecedented magnitude in the last two decades. Without a specific antiviral agent or a safe, effective and affordable vaccine for the disease, vector control remains the most effective way to control dengue transmission. The objective of this thesis is to develop spatially resolved accurate short-to-medium term dengue forecast systems, informed by mechanistic understanding of dengue transmission from previous field studies. Such systems could improve our understanding of factors that influence the transmission of dengue fever in Singapore, and potentially be used by government agencies for the planning of targeted vector control measures. / Data on dengue persistence, housing types, rainfall and seasonality was used to predict weekly dengue incidence at a housing level (i.e. dengue incidence in the high-rise and low-rise subzone groups) and at a residential subzone level. For both spatial resolutions, a separate multiple linear regression submodel was constructed for each forecast horizon of 1 to 12 weeks. Our housing-level model was able to achieve good predictions up to 6 weeks in advance, with predictive R² greater than 0.5 and total explained variance greater than 60%, but our subzone-level model was not as successful. At a housing level, we found that rainfall, housing type and seasonality predictors became relatively more important at longer forecast horizons. We also found that increased rainfall months before implies lower dengue incidence, and that rainfall influences seasonal variability in dengue incidence to a large degree. / The low-rise subzone group was also associated with higher dengue incidence than the high-rise subzone group. These findings support hypotheses from previous field studies on the roles of rainfall and urban hydrology in shaping the spatiotemporal distribution of dengue in Singapore. These risk factors of dengue could be included in current operational forecast systems to improve their predictive performance. / by Gin Kaijing Ong. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/126991 |
| Date | January 2020 |
| Creators | Ong, Gin Kaijing. |
| Contributors | Elfatih A. B. Eltahir., Massachusetts Institute of Technology. Computation for Design and Optimization Program., Massachusetts Institute of Technology. Computation for Design and Optimization Program |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 116 ages, application/pdf |
| Coverage | a-si--- |
| Rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided., http://dspace.mit.edu/handle/1721.1/7582 |
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