Machine learning in understanding environmental variability of vibriosis in coastal waters.

IF 3.7 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Applied and Environmental Microbiology Pub Date : 2025-09-17 Epub Date: 2025-08-14 DOI:10.1128/aem.00716-25
Bailey M Magers, Kyle D Brumfield, Sunil Kumar, Rita R Colwell, Antarpreet S Jutla
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引用次数: 0

Abstract

Vibrio spp. comprise ecologically significant bacteria that thrive in warm, moderately saline water, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, Vibrio spp. infections have been reported more frequently and over a greater geographical area along the US eastern seaboard. This study provides an analysis of Vibrio spp. infections, notably caused by Vibrio alginolyticus, Vibrio cholerae non-O1/non-O139, Vibrio fluvialis, Vibrio mimicus, V. parahaemolyticus, and V. vulnificus, extracted from the Centers for Disease Control and Prevention's Cholera and Other Vibrio Illness Surveillance system, located within 200 km of the eastern US coast, to analyze latitudinal distribution trends between 1990 and 2019. For each Vibrio spp., case incidence (presence/absence) was modeled, including environmental data and employing extreme gradient boosting machine learning algorithms (XGBoost). Environmental parameters associated with the incidence of vibriosis were clustered using k-means clustering. The northern limit of total cases of vibriosis was found to have increased ca. 40 km/year, with V. alginolyticus (ca. 70 km/year), V. fluvialis (ca. 60 km/year), and Vibrio parahaemolyticus (ca. 60 km/year) showing the greatest latitudinal shifts. These changes were found to be linked to environmental parameters that enhance the proliferation of Vibrio spp. The average accuracy of the XGBoost models was 60.9%-71.0%, with temperature and salinity being the most significant predictors. Relationships among other environmental parameters were complex and nonlinear, but phytoplankton and precipitation served to differentiate the models. Clustering using k-means yielded results that supported temperature, salinity, and phytoplankton as important environmental parameters. Research in progress will aid in developing global predictive risk models for Vibrio spp. infections.IMPORTANCEVibrio spp. are ecologically significant bacteria, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, Vibrio spp. infections have been reported more frequently and over a greater geographical area along the US eastern seaboard. This study provides an analysis of latitudinal distribution trends of Vibrio spp. infections, notably caused by Vibrio alginolyticus, Vibrio cholerae non-O1/non-O139, Vibrio fluvialis, Vibrio mimicus, Vibrio parahaemolyticus, and V. vulnificus, within 200 km of the eastern US coast. The northern limit of total cases of vibriosis was found to have increased ca. 40 km/year. These changes were found to be linked to environmental parameters that enhance the proliferation of Vibrio spp. Temperature and salinity were the most significant predictors of vibriosis case presence and absence. Phytoplankton and precipitation changes served to differentiate Vibrio sp. presence. Research in progress will aid in developing global predictive risk models for Vibrio spp. infections.

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机器学习在理解沿海水域弧菌病的环境变异性中的应用。
弧菌属是一种具有重要生态意义的细菌,在温暖、中等盐度的水中繁殖,其发病率和增殖受环境因素的强烈影响。近年来,据报道,沿美国东海岸的更大地理区域感染弧菌的频率更高。本研究分析了1990年至2019年美国东部沿海200公里范围内疾病控制和预防中心霍乱和其他弧菌疾病监测系统中由溶藻弧菌、非o1 /非o139霍乱弧菌、流感弧菌、模拟弧菌、副溶血性弧菌和伤口弧菌引起的弧菌感染情况,分析了纬度分布趋势。对于每种弧菌,包括环境数据和使用极端梯度增强机器学习算法(XGBoost),对病例发生率(存在/不存在)进行建模。与弧菌病发病率相关的环境参数采用k均值聚类法进行聚类。弧菌病总病例数的北部边界增加了约40 km/年,其中溶藻弧菌(约70 km/年)、河流弧菌(约60 km/年)和副溶血性弧菌(约60 km/年)的纬度变化最大。这些变化与促进弧菌增殖的环境参数有关,XGBoost模型的平均准确度为60.9% ~ 71.0%,其中温度和盐度是最显著的预测因子。其他环境参数之间的关系是复杂和非线性的,但浮游植物和降水是区分模型的重要因素。使用k-means聚类得出的结果支持温度、盐度和浮游植物作为重要的环境参数。正在进行的研究将有助于开发弧菌感染的全球预测风险模型。重要性弧菌是一种具有重要生态意义的细菌,其发病率和增殖受环境因素的强烈影响。近年来,据报道,沿美国东海岸的更大地理区域感染弧菌的频率更高。本研究分析了美国东海岸200公里范围内由溶藻弧菌、非o1 /非o139霍乱弧菌、流感弧菌、模拟弧菌、副溶血性弧菌和创伤弧菌引起的弧菌感染的纬度分布趋势。发现弧菌病总病例的北部界限每年增加约40公里。这些变化被发现与促进弧菌繁殖的环境参数有关,温度和盐度是弧菌病例存在和不存在的最重要预测因子。浮游植物和降水变化对弧菌的存在起分化作用。正在进行的研究将有助于开发弧菌感染的全球预测风险模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied and Environmental Microbiology
Applied and Environmental Microbiology 生物-生物工程与应用微生物
CiteScore
7.70
自引率
2.30%
发文量
730
审稿时长
1.9 months
期刊介绍: Applied and Environmental Microbiology (AEM) publishes papers that make significant contributions to (a) applied microbiology, including biotechnology, protein engineering, bioremediation, and food microbiology, (b) microbial ecology, including environmental, organismic, and genomic microbiology, and (c) interdisciplinary microbiology, including invertebrate microbiology, plant microbiology, aquatic microbiology, and geomicrobiology.
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