Regional Influenza Prediction with Sampling Twitter Data and PDE Model
Regional Influenza Prediction with Sampling Twitter Data and PDE Model
复制标题
DOI：
10.3390/ijerph17030678
复制
发表时间：
2020-02-01
影响因子：
--
通讯作者：
Avram, Adrian
中科院分区：
文献类型：
--
作者：
Wang, Yufang;Xu, Kuai;Avram, Adrian
大量带有地理标记的Twitter流媒体数据为研究人员提供了及时探索、建模和预测流感病例趋势的机会。然而，社交媒体数据的爆炸性增长使得数据采样成为一种自然的选择。本文提出了一种基于社交媒体实时推文数据的流感预测方法，该方法保证了预测的实时性，并适用于采样数据。具体而言，我们首先模拟流感推文的采样过程，然后开发一个特定的偏微分方程（PDE）模型来表征和预测聚合的流感推文量。我们的PDE模型结合了流感传播，流感恢复和积极的人类干预措施对减少流感的影响。我们广泛的模拟结果表明，这种偏微分方程模型几乎可以消除数据减少的影响，从抽样过程中：它需要较少的历史数据，但实现了更强的预测结果与超过90%的相对准确率在1%的抽样数据。即使对于更激进的数据采样率，如0.1%和0.01%的采样，我们的模型仍然能够分别达到85%和83%的相对准确度。这些有希望的结果突出了我们的机制PDE模型在预测流感趋势的时空模式，即使在小样本Twitter数据的情况下的能力。
The large volume of geotagged Twitter streaming data on flu epidemics provides chances for researchers to explore, model, and predict the trends of flu cases in a timely manner. However, the explosive growth of data from social media makes data sampling a natural choice. In this paper, we develop a method for influenza prediction based on the real-time tweet data from social media, and this method ensures real-time prediction and is applicable to sampling data. Specifically, we first simulate the sampling process of flu tweets, and then develop a specific partial differential equation (PDE) model to characterize and predict the aggregated flu tweet volumes. Our PDE model incorporates the effects of flu spreading, flu recovery, and active human interventions for reducing flu. Our extensive simulation results show that this PDE model can almost eliminate the data reduction effects from the sampling process: It requires lesser historical data but achieves stronger prediction results with a relative accuracy of over 90% on the 1% sampling data. Even for the more aggressive data sampling ratios such as 0.1% and 0.01% sampling, our model is still able to achieve relative accuracies of 85% and 83%, respectively. These promising results highlight the ability of our mechanistic PDE model in predicting temporal-spatial patterns of flu trends even in the scenario of small sampling Twitter data.