Digital Epidemiology has the same goal as traditional epidemiology: to understand the patterns of health and disease in a population to improve public health. Unlike traditional epidemiology, however, digital epidemiology uses data that has not been generated in the healthcare system (such as doctors and hospitals), but outside of it. Twitter is perhaps the best example of such a data source, but there are many others.
Indeed, we believe that the size of this non-traditional data already vastly exceeds that of traditional data, and will continue to grow exponentially. We thus urgently need to understand how these data sources can be mined and used for efficient epidemiological decision making.