Zhiwei Xu1, Hilary Bambrick1, Laith Yakob2, Gregor Devine3, Jiahai Lu4, Francesca D Frentiu5, Weizhong Yang6, Gail Williams7, Wenbiao Hu8. 1. School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. 2. Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK. 3. Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia. 4. School of Public Health, Sun Yat-sen University, Guangzhou, China. 5. Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia; School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia. 6. Division of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China. 7. School of Public Health, University of Queensland, Brisbane, Australia. 8. School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. Electronic address: w2.hu@qut.edu.au.
Abstract
OBJECTIVES: The burden of dengue fever in Thailand is considerable, yet there are few large-scale studies exploring the drivers of transmission. This study aimed to investigate the spatiotemporal patterns and climatic drivers of severe dengue in Thailand. METHODS: Geographic Information System (GIS) techniques and spatial cluster analysis were used to visualize the spatial distribution and detect high-risk clusters of severe dengue in 76 provinces of Thailand from January 1999 to December 2014. The seasonal patterns of severe dengue cases in different provinces were identified. A two-stage modelling approach combining a generalized linear model with a distributed lag non-linear model was used to quantify the effects of monthly mean temperature and relative humidity on the occurrence of severe dengue cases in 51 provinces of Thailand. RESULTS: Significant severe dengue clustering was detected, especially during epidemic years, and the location of these clusters showed substantial inter-annual variation. Severe dengue cases in Northern and Northeastern Thailand peaked in June to August and this pattern was stable across the study period, whereas the seasonality of severe dengue cases in other regions (especially Central Thailand) was less predictable. The risk of the occurrence of severe dengue cases increased with an increase in mean temperature in Northeastern Thailand, Central Thailand, and Southern Thailand, with peaks occurring between 24 °C to 30 °C in Northeastern Thailand and 27 °C to 29 °C in Southern Thailand West Coast, respectively. Relative humidity significantly affected the occurrence of severe dengue cases in Northeastern and Central Thailand, with optimal ranges observed for each region. CONCLUSIONS: Our findings substantiate the potential for developing climate-based dengue early warning systems for Thailand, and have implications for informing pre-emptive vector control.
OBJECTIVES: The burden of dengue fever in Thailand is considerable, yet there are few large-scale studies exploring the drivers of transmission. This study aimed to investigate the spatiotemporal patterns and climatic drivers of severe dengue in Thailand. METHODS: Geographic Information System (GIS) techniques and spatial cluster analysis were used to visualize the spatial distribution and detect high-risk clusters of severe dengue in 76 provinces of Thailand from January 1999 to December 2014. The seasonal patterns of severe dengue cases in different provinces were identified. A two-stage modelling approach combining a generalized linear model with a distributed lag non-linear model was used to quantify the effects of monthly mean temperature and relative humidity on the occurrence of severe dengue cases in 51 provinces of Thailand. RESULTS: Significant severe dengue clustering was detected, especially during epidemic years, and the location of these clusters showed substantial inter-annual variation. Severe dengue cases in Northern and Northeastern Thailand peaked in June to August and this pattern was stable across the study period, whereas the seasonality of severe dengue cases in other regions (especially Central Thailand) was less predictable. The risk of the occurrence of severe dengue cases increased with an increase in mean temperature in Northeastern Thailand, Central Thailand, and Southern Thailand, with peaks occurring between 24 °C to 30 °C in Northeastern Thailand and 27 °C to 29 °C in Southern Thailand West Coast, respectively. Relative humidity significantly affected the occurrence of severe dengue cases in Northeastern and Central Thailand, with optimal ranges observed for each region. CONCLUSIONS: Our findings substantiate the potential for developing climate-based dengue early warning systems for Thailand, and have implications for informing pre-emptive vector control.
Authors: Luong Thi Nguyen; Huy Xuan Le; Dong Thanh Nguyen; Ha Quang Ho; Ting-Wu Chuang Journal: Int J Environ Res Public Health Date: 2020-04-03 Impact factor: 3.390