  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 also the visual content of
the posted images with the help of deep convolutional neural networks. Our best
nowcasting model reached a mean absolute error of 11.33 incidents per week and
a correlation coefficient of 0.963 on the test data. Forecasting models for
predicting 1 week and 2 weeks ahead showed statistical significance as well by
reaching correlation coefficients of 0.903 and 0.862, respectively. This study
demonstrates how social media and in particular, digital photographs shared in
them, can be a valuable source of information for the field of infodemiology.
