Abstract
Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.
Highlights
Influenza is an important respiratory infectious disease causing seasonal epidemics or occasional pandemics across the world with considerable morbidity and mortality
We have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly-evolving public awareness or concern with respect to influenza transmission, and developed regression models that tracked levels of actual disease activity and can predict the influenza-like illness (ILI) activity level in a population using a delay mode
Markers and their Correlations The data set in our database consists of 881 thousand tweets containing influenza-related keywords, influenza and common cold, which were selected from 287 million Hangeul tweet timelines observed between October 2011 and September 2012
Summary
Influenza is an important respiratory infectious disease causing seasonal epidemics or occasional pandemics across the world with considerable morbidity and mortality. Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonality of influenza which is spread by airborne droplets made when an infected person coughs, sneezes or talks. Surveillance has become important to detect clusters of influenza cases and to focus public health resources on mitigating the spread and impact of the outbreaks. Since Google search engine query data were detected to be closely associated with seasonal influenza activity [2], there has been growing interest in monitoring influenza outbreaks using other digital media [3,4]
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