Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.
Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.
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DOI：
10.3390/ijerph17249378
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发表时间：
2020-12-15
影响因子：
--
通讯作者：
Laurenson A
中科院分区：
文献类型：
--
作者：
Campbell AM;Racault MF;Goult S;Laurenson A
近年来，在气候变化的背景下，海洋和沿海生态系统经历了复杂的环境变化。这些变化也反映在水传播疾病的动态中，因为这些疾病的一些病原体在水生环境中无处不在，它们的存活率受到气候条件变化的影响。先前的研究已经确定了基本气候变量与霍乱弧菌细菌的沿海分布和季节动态之间的密切关系，霍乱弧菌的致病类型是造成人类霍乱疾病的原因。在这项研究中，我们对机器学习方法的潜力进行了新的探索，以预测印度沿海地区的环境霍乱风险，该地区拥有2亿多居民，利用大气，陆地和海洋卫星衍生的基本气候变量。随机森林分类器模型的开发，训练和测试的霍乱疫情数据集在2010-2018年期间的地区沿着印度沿海。随机森林分类器模型的准确度为0.99，F1得分为0.942，灵敏度得分为0.895，这意味着89.5%的爆发被正确识别。时空模式出现在模型的性能的基础上的季节和沿海地区。对每个基本气候变量对模型输出的具体贡献的进一步分析表明，叶绿素a浓度、海面盐度和陆地表面温度是所用数据集中霍乱爆发的最强预测因子。该研究揭示了使用随机森林分类器和遥感基本气候变量开发环境霍乱风险应用程序的前景。进一步探索目前的随机森林模型和相关的基本气候变量，鼓励霍乱监测数据集在其他沿海地区受疾病的影响，以确定该模型的可转移性的潜力和应用价值的霍乱预报系统。
Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.
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影响因子：
3.7
作者：
Carvajal TM;Viacrusis KM;Hernandez LFT;Ho HT;Amalin DM;Watanabe K
通讯作者：
Watanabe K
影响因子：
5.2
作者：
Ashok, K;Guan, ZY;Yamagata, T
通讯作者：
Yamagata, T
识别印度霍乱的负担热点和危险因素：一项观察性研究。
影响因子：
3.7
作者：
Ali M;Sen Gupta S;Arora N;Khasnobis P;Venkatesh S;Sur D;Nair GB;Sack DA;Ganguly NK
通讯作者：
Ganguly NK
影响因子：
3.8
作者：
Ali M;Nelson AR;Lopez AL;Sack DA
通讯作者：
Sack DA
影响因子：
56.9
作者：
Colwell, RR
通讯作者：
Colwell, RR