Case 1: Infectious disease surveillance via social media and search (Conditions 1–4) Web-based platforms and digital social media have proliferated very widely in the recent decade, especially, but not only, in high-income countries. In 2015, 84% of all adults in the United States (Perrin & Duggan, 2015), and almost 40% of all Indians (Press Trust of India, 2015), used the Internet. Internet usage in low income countries is rapidly growing (World Bank 2017). Many people post information on their daily lives, including their health and illnesses, on social media platforms, or they use search engines to look up symptoms. This has been seen to provide epidemiology with a very powerful new data source to predict disease outbreaks, for instance by assuming that the geographical clustering of certain terms (e.g. “joint pain”, “fever”) can indicate high rates of contagion in a region. If used successfully it could help prevent or mitigate disease outbreaks and thus avoid pain, suffering, and significant cost to individuals and the public alike. Although some of these hopes and expectations have been found to be exaggerated - e.g. Google Flu Trends failed to predict the 2013 flu outbreak (Lazer et al., 2014) - the issues are mostly seen as methodological, and solvable. An early noteworthy example of DE was provided by Google through their Flu Trends programme, which predicted flu activity in 25 countries from search patterns linked to traditional disease surveillance data from public health institutions, such as the Center for Disease Control in the United States. Although the programme has since been abandoned (Lazer et al., 2014), other projects have continued with estimates of flu activity based in part upon data generated on social media platforms. Sickweather, for example, analyses scraped social media data to geographically map illness (Sickweather, 2016). Users can view anonymised reports of illness from social media down to street level, and be alerted when infectious disease outbreaks occur nearby. HealthMap provides similar services for individual users and public organisations, including tracking of flu and Ebola outbreaks (HealthMap, 2016). Google has recently engaged in similar work around the Zika virus to predict and visualize outbreaks of the disease based on weather, travel and other disease data (Google, 2016).