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Weather Impact on Heat-Related Illness in a Tropical City State, Singapore
Institute of High Performance Computing, Singapore
Institute of High Performance Computing, Singapore
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
Ministry of Health, Singapore
Urban Redevelopment Authority, Singapore
National University of Hospital, Singapore
Institute of High Performance Computing, Singapore
Rolls-Royce Singapore Pte Ltd., Singapore
National Environment Agency, Singapore
Saw Swee Hock School of Public Health, National University of Singapore, Singapore
Institute of High Performance Computing, Singapore
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- 1 Institute of High Performance Computing, Singapore
- 2 Institute of High Performance Computing, Singapore
- 3 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- 4 Ministry of Health, Singapore
- 5 Urban Redevelopment Authority, Singapore
- 6 National University of Hospital, Singapore
- 7 Institute of High Performance Computing, Singapore
- 8 Rolls-Royce Singapore Pte Ltd., Singapore
- 9 National Environment Agency, Singapore
- 10 Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- 11 Institute of High Performance Computing, Singapore
- 12 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
Atmospheric and Climate Sciences·Volume 08 (2017)·Pages 97–110·Published 29 December 2017·DOI10.4236/acs.2018.81007
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Abstract
In this article we propose a novel hurdle negative binomial (HNB) regression combined with a distributed lag nonlinear model (DLNM) to model weather factors’ impact on heat related illness (HRI) in Singapore. AIC criterion is adopted to help select proper combination of weather variables and check their lagged effect as well as nonlinear effect. The process of model selection and validation is demonstrated. It is observed that the predicted occurrence rate is close to the observed one. The proposed combined model can be used to predict HRI cases for mitigating HRI occurrences and provide inputs for related public health policy considering climate change impact.
KeywordsDistributed Lag Nonlinear ModelHeat-Related IllnessHurdle ModelNegative Binomial distributionWeather Factors
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