Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhua Road, Lixia District, Jinan, 250012, People's Republic of China.
State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing, 102206, People's Republic of China.
Infect Dis Poverty. 2023 Apr 21;12(1):42. doi: 10.1186/s40249-023-01093-0.
Global connectivity and environmental change pose continuous threats to dengue invasions from worldwide to China. However, the intrinsic relationship on introduction and outbreak risks of dengue driven by the landscape features are still unknown. This study aimed to map the patterns on source-sink relation of dengue cases and assess the driving forces for dengue invasions in China.
We identified the local and imported cases (2006-2020) and assembled the datasets on environmental conditions. The vector auto-regression model was applied to detect the cross-relations of source-sink patterns. We selected the major environmental drivers via the Boruta algorithm to assess the driving forces in dengue outbreak dynamics by applying generalized additive models. We reconstructed the internal connections among imported cases, local cases, and external environmental drivers using the structural equation modeling.
From 2006 to 2020, 81,652 local dengue cases and 12,701 imported dengue cases in China were reported. The hotspots of dengue introductions and outbreaks were in southeast and southwest China, originating from South and Southeast Asia. Oversea-imported dengue cases, as the Granger-cause, were the initial driver of the dengue dynamic; the suitable local bio-socioecological environment is the fundamental factor for dengue epidemics. The Bio8 [odds ratio (OR) = 2.11, 95% confidence interval (CI): 1.67-2.68], Bio9 (OR = 291.62, 95% CI: 125.63-676.89), Bio15 (OR = 4.15, 95% CI: 3.30-5.24), normalized difference vegetation index in March (OR = 1.27, 95% CI: 1.06-1.51) and July (OR = 1.04, 95% CI: 1.00-1.07), and the imported cases are the major drivers of dengue local transmissions (OR = 4.79, 95% CI: 4.34-5.28). The intermediary effect of an index on population and economic development to local cases via the path of imported cases was detected in the dengue dynamic system.
Dengue outbreaks in China are triggered by introductions of imported cases and boosted by landscape features and connectivity. Our research will contribute to developing nature-based solutions for dengue surveillance, mitigation, and control from a socio-ecological perspective based on invasion ecology theories to control and prevent future dengue invasion and localization.
全球连通性和环境变化持续威胁着登革热从世界范围传入中国。然而，由景观特征驱动的登革热输入和暴发风险的内在关系尚不清楚。本研究旨在绘制登革热病例源汇关系图，并评估中国登革热传入和暴发的驱动力。
我们确定了本地和输入病例（2006-2020 年），并组装了环境条件数据集。应用向量自回归模型检测源汇模式的交叉关系。我们通过 Boruta 算法选择主要环境驱动因素，通过广义加性模型评估登革热暴发动态的驱动因素。我们使用结构方程模型重建输入病例、本地病例和外部环境驱动因素之间的内部联系。
2006 年至 2020 年，中国报告了 81652 例本地登革热病例和 12701 例输入登革热病例。登革热输入和暴发的热点位于中国东南部和西南部，源自南亚和东南亚。海外输入的登革热病例作为格兰杰因果关系，是登革热动态的初始驱动因素；适宜的本地生物社会生态环境是登革热流行的根本因素。生物 8 [比值比（OR）=2.11，95%置信区间（CI）：1.67-2.68]、生物 9（OR=291.62，95%CI：125.63-676.89）、生物 15（OR=4.15，95%CI：3.30-5.24）、3 月归一化植被指数（OR=1.27，95%CI：1.06-1.51）和 7 月归一化植被指数（OR=1.04，95%CI：1.00-1.07）以及输入病例是本地传播登革热的主要驱动因素（OR=4.79，95%CI：4.34-5.28）。在登革热动态系统中，检测到人口和经济发展等指标通过输入病例对本地病例的中介效应。
中国登革热暴发是由输入病例引发的，受景观特征和连通性的推动。我们的研究将有助于基于入侵生态学理论从社会生态角度为登革热监测、缓解和控制制定基于自然的解决方案，以控制和预防未来的登革热传入和本地化。
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