A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico
A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico
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DOI：
10.1109/access.2020.2980634
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Malerba, Donato
中科院分区：
文献类型：
--
作者：
Appice, Annalisa;Gel, Yulia R.;Malerba, Donato
蚊子传播的登革热是热带国家的一个主要公共卫生问题，在那里它受到温度等气候因素的强烈影响。在本文中，我们制定了一个整体的机器学习策略来分析温度和登革热数据的时间动态，并利用这些知识根据年度温度对登革热进行准确的预测。利用一种新的多阶段组合的自动编码，基于窗口的数据表示和基于趋势的时间聚类从历史数据中提取的时间动态。该预测是用基于趋势关联的最近邻预测器来执行的。所提出的战略的有效性进行了评估的案例研究，其中包括登革热和登革出血热病例的数量在1985年和2013年期间收集的32个联邦州的墨西哥。实证研究证明了所提出的策略的可行性，并证实它优于各种国家的最先进的竞争对手的方法，制定回归和时间序列预测分析。
The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985 & x2013;2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis.