A Novel Data-Driven Model for Real-Time Influenza Forecasting
A Novel Data-Driven Model for Real-Time Influenza Forecasting
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DOI：
10.1109/access.2018.2888585
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Nichols, Stephen
中科院分区：
文献类型：
--
作者：
Venna, Siva R.;Tavanaei, Amirhossein;Nichols, Stephen
我们提出了一种新的数据驱动的机器学习方法，使用基于长短期记忆（LSTM）的多阶段预测进行流感预测。该方法的新颖之处包括：1）引入LSTM方法来捕捉季节性流感的时间动态; 2）捕捉外部变量影响的技术，包括地理邻近度和气候变量，如湿度、温度、降水和日照。所提出的模型进行比较，对两个国家的最先进的技术，使用两个公开的数据集。我们提出的方法比现有的知名流感预测方法更好。研究结果为使用数据驱动的预测方法以及捕捉时空和环境因素的影响以改善流感预测提供了一个有希望的方向。
We propose a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting. The novel aspects of the method include the following: 1) the introduction of LSTM method to capture the temporal dynamics of seasonal flu and 2) a technique to capture the influence of external variables that includes the geographical proximity and climatic variables such as humidity, temperature, precipitation, and sun exposure. The proposed model is compared against two state-of-the-art techniques using two publicly available datasets. Our proposed method performs better than the existing well-known influenza forecasting methods. The results offer a promising direction in terms of both using the data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors to improve influenza forecasting.