Online user-generated data (search engine activity, social media) offers health-related insights that traditional health monitoring systems cannot always provide. A central paradigm for this has been the estimation of the rate of a disease in a population by modelling the corresponding online search engine activity. However, the various inaccuracies in the estimates of Google Flu Trends (GFT) ―a popular approach for estimating influenza-like illness (ILI) rates from online search logs― have generated a debate as to whether this is something feasible. In this talk, we will first go through GFT and try to explain why it has been unsuccessful. We will then present alternative approaches which deploy more adequate statistical as well as semantic modelling techniques to successfully capture ILI rates both in the US and England. Long-term empirical analysis will showcase that models retain a strong predictive performance across many consecutive flu seasons. Expanding on the previous approaches, we will also present a brief overview of more recent work, where multi-task learning across different geographies is used to further improve ILI models, especially in situations where sporadic historical health reports are available.