Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors
Dengue, a climate-sensitive mosquito-borne viral disease, is endemic in many tropical and subtropical areas, with Southeast Asia bearing the highest burden. In China, dengue epidemics are primarily influenced by imported cases from Southeast Asia. By integrating monthly maximum temperature and preci...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325000879 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856914055823360 |
|---|---|
| author | Shaowei Sang Yiguan Wang Qiyong Liu Peng Chen Chuanxi Li Anran Zhang |
| author_facet | Shaowei Sang Yiguan Wang Qiyong Liu Peng Chen Chuanxi Li Anran Zhang |
| author_sort | Shaowei Sang |
| collection | DOAJ |
| description | Dengue, a climate-sensitive mosquito-borne viral disease, is endemic in many tropical and subtropical areas, with Southeast Asia bearing the highest burden. In China, dengue epidemics are primarily influenced by imported cases from Southeast Asia. By integrating monthly maximum temperature and precipitation from Southeast Asia and local provinces in China, we aim to build models to predict dengue incidence in high-risk areas of China. From 2005–2023, a total of 117,839 dengue cases were reported, with Guangdong and Yunnan provinces accounting for 57.8 % and 26.2 % of cases, respectively. Large outbreaks occurred in 2014 (47,052 cases), 2019 (22,688 cases), and 2023 (19,936 cases), with peak incidence typically observed from August to October. The number of provinces reporting cases and outbreaks gradually linearly increased from 10 and 0 in 2005 to a peak of 30 and 11 in 2019, respectively, before declining in 2021 and 2022, then rebounding to 29 and 8 in 2023. Of the 13,927 imported cases, 91.3 % were from Southeast Asia, primarily from Myanmar (41.3 %) and Cambodia (27.3 %). Predictive models for dengue incidence in Guangdong Province showed high adjusted R2 values (0.993–0.999) and deviance explained values (0.975–0.989). The models from Cambodia, Thailand, and the Philippines outperformed the other six models. In Yunnan, adjusted R2 values and deviance explained values ranged from 0.81–0.84 and 0.90–0.92, respectively, with models from Laos, Myanmar and Cambodia achieving the best predictive performance. Incorporating meteorological data from Southeast Asia along with local data from China, we were able to develop accurate predictive models for dengue incidence in the high-risk areas of China. |
| format | Article |
| id | doaj-art-80c06ace51d54cdea4b0c37043af0c7e |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-80c06ace51d54cdea4b0c37043af0c7e2025-02-12T05:30:11ZengElsevierEcotoxicology and Environmental Safety0147-65132025-01-01290117751Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factorsShaowei Sang0Yiguan Wang1Qiyong Liu2Peng Chen3Chuanxi Li4Anran Zhang5Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong, PR China; Correspondence to: No. 107 Wenhua Road, Lixia District, Jinan, Shandong 250012, PR China.CAS Key Laboratory of Insect Developmental and Evolutionary Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai, PR ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Vector Surveillance and Management, Beijing, PR ChinaDepartment of Healthcare-associated Infection Management, School and Hospital of Stomatology, Shandong University, Jinan, Shandong, PR ChinaClinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong, PR ChinaClinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong, PR ChinaDengue, a climate-sensitive mosquito-borne viral disease, is endemic in many tropical and subtropical areas, with Southeast Asia bearing the highest burden. In China, dengue epidemics are primarily influenced by imported cases from Southeast Asia. By integrating monthly maximum temperature and precipitation from Southeast Asia and local provinces in China, we aim to build models to predict dengue incidence in high-risk areas of China. From 2005–2023, a total of 117,839 dengue cases were reported, with Guangdong and Yunnan provinces accounting for 57.8 % and 26.2 % of cases, respectively. Large outbreaks occurred in 2014 (47,052 cases), 2019 (22,688 cases), and 2023 (19,936 cases), with peak incidence typically observed from August to October. The number of provinces reporting cases and outbreaks gradually linearly increased from 10 and 0 in 2005 to a peak of 30 and 11 in 2019, respectively, before declining in 2021 and 2022, then rebounding to 29 and 8 in 2023. Of the 13,927 imported cases, 91.3 % were from Southeast Asia, primarily from Myanmar (41.3 %) and Cambodia (27.3 %). Predictive models for dengue incidence in Guangdong Province showed high adjusted R2 values (0.993–0.999) and deviance explained values (0.975–0.989). The models from Cambodia, Thailand, and the Philippines outperformed the other six models. In Yunnan, adjusted R2 values and deviance explained values ranged from 0.81–0.84 and 0.90–0.92, respectively, with models from Laos, Myanmar and Cambodia achieving the best predictive performance. Incorporating meteorological data from Southeast Asia along with local data from China, we were able to develop accurate predictive models for dengue incidence in the high-risk areas of China.http://www.sciencedirect.com/science/article/pii/S0147651325000879DengueImported casesSoutheast AsiaMeteorological factorsPredictive model |
| spellingShingle | Shaowei Sang Yiguan Wang Qiyong Liu Peng Chen Chuanxi Li Anran Zhang Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors Ecotoxicology and Environmental Safety Dengue Imported cases Southeast Asia Meteorological factors Predictive model |
| title | Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors |
| title_full | Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors |
| title_fullStr | Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors |
| title_full_unstemmed | Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors |
| title_short | Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors |
| title_sort | predicting dengue incidence in high risk areas of china through the integration of southeast asian and local meteorological factors |
| topic | Dengue Imported cases Southeast Asia Meteorological factors Predictive model |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325000879 |
| work_keys_str_mv | AT shaoweisang predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors AT yiguanwang predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors AT qiyongliu predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors AT pengchen predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors AT chuanxili predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors AT anranzhang predictingdengueincidenceinhighriskareasofchinathroughtheintegrationofsoutheastasianandlocalmeteorologicalfactors |