Predicting the Flu from Instagram
Oguzhan Gencoglu, Miikka Ermes

TL;DR
This study demonstrates that Instagram data, including images and hashtags, can effectively predict influenza-like illness trends, offering a viable alternative to traditional surveillance methods.
Contribution
It introduces a novel approach using Instagram's visual and textual data for influenza prediction, incorporating deep learning for image analysis.
Findings
Best nowcasting MAE of 11.33 incidents/week
Correlation coefficient of 0.963 for nowcasting
Significant forecasting correlations of 0.903 and 0.862 for 1- and 2-week ahead predictions
Abstract
Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economically. Search engine queries and Twitter have been the primarily used data sources in such approaches. The aim of this study is to assess the predictive power of an alternative data source, Instagram. By using 317 weeks of publicly available data from Instagram, we trained several machine learning algorithms to both nowcast and forecast the number of official influenza-like illness incidents in Finland where population-wide official statistics about the weekly incidents are available. In addition to date and hashtag count features of online posts, we were able to utilize…
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Taxonomy
TopicsData-Driven Disease Surveillance · Misinformation and Its Impacts · Respiratory viral infections research
