Severe pandemics due to highly‐transmissible viruses continue to threaten the world in the 21st century. In a tightly interconnected world, infectious disease outbreaks can adversely affect economic growth, trade, tourism, business and industry, and social stability as well as public and population health. Public health authorities and researchers now collect data from many sources, and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns.

Tracking of internet-based health indicators complements other surveillance methods collecting data from clinical systems and registries. To provide proper alerts and timely response public health officials and researchers systematically gather news, and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources. With the advent of modern communication technologies, many outbreak reports now originate in electronic media and electronic discussion groups. Given the ever increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Web applications along with text processing programs are increasingly being used to harness online data and information to discover meaningful patterns identifying emerging health threats. The advances in web science and technology for data management, integration, mining, classification, filtering and visualization has given rise to variety of applications representing real-time data on epidemics.

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