温度和湿度是人畜共患疾病传播的驱动因素

Li Zhang, Chenrui Lv, Wenqiang Guo, Zhenzhuo Li
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摘要

人畜共患病仍然是全球公共卫生的一个长期威胁。许多主要的人畜共患病原体表现出与气候变异相关的季节性模式。量化温度和湿度等环境变量对疾病传播动态的影响对于改进预测和控制措施至关重要。本综述综述了温度和湿度与疟疾、登革热、狂犬病、疟原虫病和流感等主要人畜共患病之间关系的现有证据。总体而言,本综述强调了所研究的不同人畜共患病的一些总体主题。在适宜的温度范围内,较高的温度通常会增加传播风险,而过高或过低的温度则会产生不利影响。湿度表现出复杂的非线性关系,在某些温度区域会促进传播,但在其他区域则会抑制传播。暴雨和高湿度有利于病媒繁殖,从而与疟疾等病媒传播疾病的爆发有关。然而,在降雨量大的情况下,登革热等一些疾病的发病率也有所降低。为了弥补现有的知识差距,未来的研究工作应优先考虑以下几个关键领域:通过强有力的监测和整合高分辨率微气候数据来提高数据质量;实现分析框架标准化并利用机器学习等先进方法;开展机理研究以阐明病原体、病媒和宿主对气候刺激的反应;采用跨学科方法来考虑相互作用的驱动因素;以及预测各种气候变化情景下的疾病影响,为适应战略提供信息。对这些研究重点进行投资,可以推动开发以证据为基础、具有气候意识的疾病预测和控制措施,最终更有效地保障公众健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Temperature and humidity as drivers for the transmission of zoonotic diseases

Temperature and humidity as drivers for the transmission of zoonotic diseases

Zoonotic diseases remain a persistent threat to global public health. Many major zoonotic pathogens exhibit seasonal patterns associated with climatic variations. Quantifying the impacts of environmental variables such as temperature and humidity on disease transmission dynamics is critical for improving prediction and control measures. This review synthesizes current evidence on the relationships between temperature and humidity and major zoonotic diseases, including malaria, dengue, rabies, anisakiasis, and influenza. Overall, this review highlighted some overarching themes across the different zoonotic diseases examined. Higher temperatures within suitable ranges were generally associated with increased transmission risks, while excessively high or low temperatures had adverse effects. Humidity exhibited complex nonlinear relationships, facilitating transmission in certain temperature zones but inhibiting it in others. Heavy rainfall and high humidity were linked to vector-borne disease outbreaks such as malaria by enabling vector breeding. However, reduced incidence of some diseases like dengue fever was observed with high rainfall. To address existing knowledge gaps, future research efforts should prioritize several key areas: enhancing data quality through robust surveillance and the integration of high-resolution microclimate data; standardizing analytical frameworks and leveraging advanced methodologies such as machine learning; conducting mechanistic studies to elucidate pathogen, vector, and host responses to climatic stimuli; adopting interdisciplinary approaches to account for interacting drivers; and projecting disease impacts under various climate change scenarios to inform adaptation strategies. Investing in these research priorities can propel the development of evidence-based climate-aware disease prediction and control measures, ultimately safeguarding public health more effectively.

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