Yin, Shi, Hua, Junyi, Ren, Chao, Guénard, Benoit, Wang, Runxi, Weemaels, André Ibáñez, Shi, Yuan 
ORCID: 0000-0003-4011-8735, Lee, Tsz-Cheung, Yuan, Hsiang-Yu, Chong, Marc Ka-chun et al (show 1 more authors)
(2023)
Mapping dengue fever risk for a non-endemic high-density city in subtropical region
EGU General Assembly 2023.
Abstract
Dengue fever is a mosquito-borne disease caused by the dengue virus bringing huge health burdens in tropical regions. With global warming, rapid urbanization, and mosquito species introductions, the range of dengue fever is expected to expand to subtropical regions and increase potential health risks for local populations. To reduce dengue fever transmission, relevant risk map is one of the most effective tools for public health management. Though there is abundant literature about mapping the dengue fever risks in endemic regions, few studies in contrast have investigated dengue fever risks for non-endemic regions; hindering the development of preparedness planning.In this study, the spatial hazard-exposure-vulnerability assessment framework proposed by the Intergovernmental Panel on Climate Change was applied in to detect the dengue fever risk in Hong Kong, which is a typical high-density city located within a subtropical region. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, a mosquito species common in Hong Kong and proxy for the potential dengue fever hazard, was predicted using MaxEnt models relying on the surveillance data and a list of variables related to urban morphology, landscape, land utilization, and local climate. Secondly, the bivariate local Moran’s I was measured to identify urban areas with both high dengue hazard and high human population exposure. Then, vulnerable groups among the human population were identified from the 2016 Hong Kong census data. Finally, dengue risks were assessed at the community scale by overlapping the results of hazard, exposure, and vulnerability analysis.In the optimal MaxEnt model predicting the presence possibility of Aedes albopictus, the normalized difference vegetation index, frontal area index, and the aggregation index of public residential land ranked the top three among all predictors, with permutation importance of 31.8%, 22.8%, and 17% respectively. Three components were generated after principal component analysis on the vulnerable groups. Lastly, this approach allowed the identification of 17 high-risk spots within Hong Kong. In addition, the underlying factors behind each hot spot were investigated from the aspects of hazard, exposure, and vulnerability respectively, and specific suggestions for dengue prevention were proposed accordingly.The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.This study was funded by the Health and Medical Research Fund of the Food and Health Bureau (No. 20190672).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 4101 Climate Change Impacts and Adaptation, 41 Environmental Sciences, Infectious Diseases, Rare Diseases, Emerging Infectious Diseases, Climate-Related Exposures and Conditions, Biodefense, Prevention, Vector-Borne Diseases, 2.2 Factors relating to the physical environment, 3.2 Interventions to alter physical and biological environmental risks, Infection, 3 Good Health and Well Being, 15 Life on Land |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 01 Jul 2024 09:41 |
| Last Modified: | 23 May 2026 08:43 |
| DOI: | 10.5194/egusphere-egu23-2994 |
| Open Access URL: | https://meetingorganizer.copernicus.org/EGU23/EGU2... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3182550 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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