Abstract
BackgroundInfluenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection. Furthermore, accumulating influenza related data is not easy, because the type of data that is associated with influenza is very limited. For these reasons, identifying useful data and building a prediction model with these data are necessary steps toward predicting if the number of patients will increase or decrease. On the Internet, numerous press releases are published every day that reflect currently pending issues.ResultsIn this research, we collected Internet articles related to infectious diseases from the Centre for Health Protection (CHP), which is maintained the by Hong Kong Department of Health, to see if news text data could be used to predict the spread of influenza. In total, 7769 articles related to infectious diseases published from 2004 January to 2018 January were collected. We evaluated the predictive ability of article text data from the period of 2013–2018 for each of the weekly time horizons. The support vector machine (SVM) model was used for prediction in order to examine the use of information embedded in the web articles and detect the pattern of influenza spread variance. The prediction result using news text data with SVM exhibited a mean accuracy of 86.7 % on predicting whether weekly ILI patient ratio would increase or decrease, and a root mean square error of 0.611 on estimating the weekly ILI patient ratio.ConclusionsIn order to remedy the problems of conventional data, using news articles can be a suitable choice, because they can help estimate if ILI patient ratio will increase or decrease as well as how many patients will be affected, as shown in the result of research. Thus, advancements in research on using news articles for influenza prediction should continue to be pursed, as the result showed acceptable performance as compared to existing influenza prediction researches.
Highlights
Influenza continues to pose a serious threat to human health worldwide
In order to remedy the problems of conventional data, in this study, features related to influenza spread are extracted from news articles provided by the Internet, and these are used to detect the variance in the number of influenza patients
In this research, in order to predict the number of influenza patients in the United States, we used news article data, which is easier to collect than traditional data for influenza prediction, such as climatic or clinical data
Summary
Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. It is not easy to accumulate data for research, because only a few different types of data exist that are related to the spread of influenza to use as variables to predict the spread of influenza; further, since this data is often relevant to patients, it is necessary to collect data from patients and agencies with confidentiality agreements. Even if these types of data are collectable, it is almost impossible to immediately obtain the latest data in a usable form. If data that can be used for influenza prediction can be discovered, they would prove very useful
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