Keke Liu1, Jimin Sun2, Xiaobo Liu1, Ruiyun Li3, Yiguan Wang4, Liang Lu1, Haixia Wu1, Yuan Gao1, Lei Xu5, Qiyong Liu6. 1. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. 2. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China. 3. Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway. 4. School of Biological Sciences, University of Queensland, QLD 4072, Australia. 5. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway. Electronic address: lei.xu@ibv.uio.no. 6. State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. Electronic address: liuqiyong@icdc.cn.
Abstract
OBJECTIVE: To identify the high risk spatiotemporal clusters of dengue cases and explore the associated risk factors. METHODS: Monthly indigenous dengue cases in 2005-2017 were aggregated at county level. Spatiotemporal cluster analysis was used to explore dengue distribution features using SaTScan9.4.4 and Arcgis10.3.0. In addition, the influential factors and potential high risk areas of dengue outbreaks were analyzed using ecological niche models in Maxent 3.3.1 software. RESULTS: We found a heterogeneous spatial and temporal distribution pattern of dengue cases. The identified high risk region in the primary cluster covered 13 counties in Guangdong Province and in the secondary clusters included 14 counties in Yunnan Province. Additionally, there was a nonlinear association between meteorological and environmental factors and dengue outbreaks, with 8.5%-57.1%, 6.7%-38.3% and 3.2%-40.4% contribution from annual average minimum temperature, land cover and annual average precipitation, respectively. CONCLUSIONS: The high risk areas of dengue outbreaks mainly are located in Guangdong and Yunnan Provinces, which are significantly shaped by environmental and meteorological factors, such as temperature, precipitation and land cover.
OBJECTIVE: To identify the high risk spatiotemporal clusters of dengue cases and explore the associated risk factors. METHODS: Monthly indigenous dengue cases in 2005-2017 were aggregated at county level. Spatiotemporal cluster analysis was used to explore dengue distribution features using SaTScan9.4.4 and Arcgis10.3.0. In addition, the influential factors and potential high risk areas of dengue outbreaks were analyzed using ecological niche models in Maxent 3.3.1 software. RESULTS: We found a heterogeneous spatial and temporal distribution pattern of dengue cases. The identified high risk region in the primary cluster covered 13 counties in Guangdong Province and in the secondary clusters included 14 counties in Yunnan Province. Additionally, there was a nonlinear association between meteorological and environmental factors and dengue outbreaks, with 8.5%-57.1%, 6.7%-38.3% and 3.2%-40.4% contribution from annual average minimum temperature, land cover and annual average precipitation, respectively. CONCLUSIONS: The high risk areas of dengue outbreaks mainly are located in Guangdong and Yunnan Provinces, which are significantly shaped by environmental and meteorological factors, such as temperature, precipitation and land cover.
Authors: Syed Ali Asad Naqvi; Muhammad Sajjad; Liaqat Ali Waseem; Shoaib Khalid; Saima Shaikh; Syed Jamil Hasan Kazmi Journal: Int J Environ Res Public Health Date: 2021-11-16 Impact factor: 3.390