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arXiv · 2111.10675

Detecting Influenza Epidemics on Twitter

Katerina Katsani-Geronymaki·Polyvios Pratikakis
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Abstract

This paper presents a predictive model for Influenza-Like-Illness, based on Twitter traffic. We gather data from Twitter based on a set of keywords used in the Influenza wikipedia page, and perform feature selection over all words used in 3 years worth of tweets, using real ILI data from the Greek CDC. We select a small set of words with high correlation to the ILI score, and train a regression model to predict the ILI score cases from the word features. We deploy this model on a streaming application and feed the resulting time-series to FluHMM, an existing prediction model for the phases of the epidemic. We find that Twitter traffic offers a good source of information and can generate early warnings compared to the existing sentinel protocol using a set of associated physicians all over Greece.

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Katerina Katsani-Geronymaki, Polyvios Pratikakis. 2021-11-20. Detecting Influenza Epidemics on Twitter. https://arxiv.org/abs/2111.10675

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