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气候变化与:预测性风险评估的环境决定因素。 (原标题表述不太完整规范,正常完整标题可能会更清晰准确些)

Climate change and : Environmental determinants for predictive risk assessment.

作者信息

Brumfield Kyle D, Usmani Moiz, Long Daniel M, Lupari Henry A, Pope Robert K, Jutla Antarpreet S, Huq Anwar, Colwell Rita R

机构信息

Department of Cellular Biology and Molecular Genetics, Maryland Pathogen Research Institute, University of Maryland, College Park, MD 20742.

Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742.

出版信息

Proc Natl Acad Sci U S A. 2025 Aug 19;122(33):e2420423122. doi: 10.1073/pnas.2420423122. Epub 2025 Aug 11.

Abstract

Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic ., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant . and other waterborne pathogens.

摘要

气候变化对微生物的发病率和丰度有重大影响,包括那些对环境循环至关重要的微生物以及那些对人类和动物致病的微生物。有利于微生物生长的条件变化扩大了许多病原体的地理范围,导致传染病的出现和再次出现。在不利气候条件与人口脆弱性(特别是水、环境卫生和个人卫生基础设施不足)相互交织的地区,水传播疾病构成严重风险。由于许多水传播病原体在环境中发挥着关键作用,如在碳和氮循环中,因此无法根除它们。然而,整合微生物学、社会学和气象数据,识别有利于致病菌株生长的环境启发因素的预测智能模型,可以提供前瞻性决策能力,降低感染风险。在此,目标是分析自20世纪60年代以来的研究数据,以确定驱动致病性……发生和分布的环境决定因素,从而能够对气候变化对霍乱和非霍乱弧菌病的影响进行预测建模。……在水生生态系统中的增殖与气候变化相关,同时也与环境疾病传播增加有关,特别是东南亚和非洲部分地区的霍乱以及北欧和北美东海岸的非霍乱弧菌病。全球……预测风险模型与疫情早期的个体和大规模干预相结合,有助于降低病死率。这些模型经过适当修改后,有可能预测由所有临床相关的……和其他水传播病原体引起的疾病。

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