Abstract
Influenza causes numerous deaths worldwide every year. Predicting the number of influenza patients is an important task for medical institutions. Two types of data regarding influenza-like illnesses (ILIs) are often used for flu prediction: (1) historical data and (2) user generated content (UGC) data on the web such as search queries and tweets. Historical data have an advantage against the normal state but show disadvantages against irregular phenomena. In contrast, UGC data are advantageous for irregular phenomena. So far, no effective model providing the benefits of both types of data has been devised. This study proposes a novel model, designated the two-stage model, which combines both historical and UGC data. The basic idea is, first, basic regular trends are estimated using the historical data-based model, and then, irregular trends are predicted by the UGC data-based model. Our approach is practically useful because we can train models separately. Thus, if a UGC provider changes the service, our model could produce better performance because the first part of the model is still stable. Experiments on the US and Japan datasets demonstrated the basic feasibility of the proposed approach. In the dropout (pseudo-noise) test that assumes a UGC service would change, the proposed method also showed robustness against outliers. The proposed model is suitable for prediction of seasonal flu.
Highlights
Seasonal influenza epidemics, representing severe infectious diseases, are characterized by the widespread incidence of various symptoms such as a sudden onset of fever, cough, headache, and muscle and joint pain
The Two-stage model achieved the best performance for several periods and metrics in the US and achieved the best performance among all metrics in Japan, demonstrating that the Twostage model is better for flu prediction compared to the other two models
Throughout the experiments, we demonstrate that historical influenza-like illnesses (ILIs) data and user generated content (UGC) data are useful for flu prediction, which is consistent with the results of previous studies [3, 6, 18]
Summary
Seasonal influenza epidemics, representing severe infectious diseases, are characterized by the widespread incidence of various symptoms such as a sudden onset of fever, cough, headache, and muscle and joint pain. The World Health Organization (WHO) reported that 3–5 million cases of severe illness occur worldwide each year because of seasonal influenza, leading to approximately 290,000—650,000 deaths annually [1]. Seasonal influenza affects the economic productivity because of employee absence and unexpected increases in hospital work loads [2]. Prediction of influenza outbreaks is crucially important to support realtime decision-making related to the management of hospital resources with a rapid response.
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