Zhou Jiayi, Yao Yunchong, Li Lingling, Wang Xu, Dai Tingting, Cai Xiaoyan, Wang Lingxi, She Yueqin, Zhang Xingxing, Zhang Jinhui, Zhou Haijian, Wu Haisheng, Guo Pi
Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, 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, Beijing 102206, China.
J Infect. 2025 Jul;91(1):106518. doi: 10.1016/j.jinf.2025.106518. Epub 2025 May 23.
Infectious diarrheal diseases continue to impose a heavy public health burden in China, despite significant advancements in sanitation and economic development. While existing evidence has linked climate factors to the dynamics of these diseases, the heterogeneous climatic conditions and complex nonlinear interactions among meteorological variables give rise to intricate epidemic patterns that complicate the identification of causal drivers underlying the observed spatial and temporal variability in disease incidence. To address this gap, we conducted a nationwide study across 365 city-level regions in China from 2005 to 2022. Based on high-resolution surveillance data and meteorological records, we applied an empirical dynamic modeling framework. We inferred causal links between climatic drivers and six notifiable infectious diarrheal diseases using convergent cross-mapping, and further assessed the dynamic impacts of these drivers through multivariate forecast improvement and scenario exploration across different climatic zones. Our results reveal that, except for cholera, infectious diarrheal diseases are predominantly influenced by temperature, relative humidity, and sunshine hours. Temperature generally promotes the incidence of typhoid fever, bacillary dysentery, and other infectious diarrhea, while the influence of relative humidity and sunshine hours varies with environmental context. This study not only characterizes the epidemiological trends of infectious diarrhea over nearly two decades but also demonstrates the feasibility of using EDM to uncover dynamic nonlinear interactions in climate-disease systems. By integrating empirical dynamic modeling into public health frameworks, our approach provides a scalable and effective tool for disentangling complex climate-disease interactions in a warming world, thereby informing more tailored public health interventions in response to climate change.
尽管中国在环境卫生和经济发展方面取得了显著进步,但感染性腹泻疾病仍然给公共卫生带来沉重负担。虽然现有证据已将气候因素与这些疾病的动态变化联系起来,但气候条件的异质性以及气象变量之间复杂的非线性相互作用,导致了复杂的流行模式,使得难以确定观察到的疾病发病率时空变化背后的因果驱动因素。为了填补这一空白,我们于2005年至2022年在中国365个城市地区开展了一项全国性研究。基于高分辨率监测数据和气象记录,我们应用了一个经验动态建模框架。我们使用收敛交叉映射推断气候驱动因素与六种法定报告感染性腹泻疾病之间的因果联系,并通过多变量预测改进和不同气候区的情景探索,进一步评估这些驱动因素的动态影响。我们的结果表明,除霍乱外,感染性腹泻疾病主要受温度、相对湿度和日照时数的影响。温度通常会促进伤寒、细菌性痢疾和其他感染性腹泻的发病率,而相对湿度和日照时数的影响则因环境背景而异。这项研究不仅描述了近二十年来感染性腹泻的流行病学趋势,还证明了使用经验动态建模来揭示气候-疾病系统中动态非线性相互作用的可行性。通过将经验动态建模整合到公共卫生框架中,我们的方法提供了一种可扩展且有效的工具,用于在气候变暖的世界中解开复杂的气候-疾病相互作用,从而为应对气候变化制定更具针对性的公共卫生干预措施提供依据。