Trends detection of flu based on ensemble models with emotional factors from social networks

Abstract

Influenza is an acute respiratory illness and widespread activity that occurs every year. Detection and prevention of influenza in its earliest stage would reduce the spread range of the illness. Sina microblog is a popular microblogging service in China, which can be treated as perfect reference sources for flu detection because of its real-time character. A large number of active users post about their daily life continually. In this paper, we investigate the real-time flu detection problem and propose a flu detection model with emotion factors and semantic information. First, we extract flu-related microblog posts automatically in real time by adopting support vector machine (SVM) filter and semantic features. We use association rule mining to extract strongly associated features as additional features for posts to overcome the limitation of 140 words, including sentiment information, which can help us to classify the posts without flu-related features. Then, the conditional random field model is revised and applied to detect the transition time of flu so that we can find out which place is more likely to have influenza outbreak and when it is more likely to have influenza outbreak in a city or province in China. Experimental results on detecting flu situation during certain times in some locations show the robustness and effectiveness of the proposed model, which might help health authorities in predicting flu outbreak ahead and take timely control action and response.

The Prediction of Infectious Diseases: A Bibliometric Analysis

“…According to Figure 3 , it can be found science and technology-other topics is the top 3 subject category. Recently, some studies have discussed the emerging technologies to predict infectious diseases, including remote sensing technology [ 67 ], artificial intelligence [ 68 ], big data analysis [ 69 , 70 ], social media [ 71 ]. The emergence of new technologies is important for researches and scholars to improve the prediction precision of infectious diseases.…”