Workshop on Big Data and Analytics for Infectious Disease Research, Operations, and Policy
With the amount of data in our world exploding, big data could generate significant value in the field of infectious disease. The increased use of social media provides an opportunity to improve public health surveillance systems and develop predictive models. Advances in machine learning and crowdsourcing may also offer the possibility to gather information about disease dynamics, such as contact patterns and the impact of the social environment. New, rapid, point-of-care diagnostics may make it possible to not only capture diagnostic information but also other potentially epidemiologically relevant information in real time. With a wide range of data available for analysis, decision-making and policy-making processes could be improved. The broader applications and implications of big data in these areas need to be explored.
While there are many opportunities for big data to be used for infectious disease research, operations, and policy, many challenges need to be addressed to capture the full potential of big data. Specifically, there are questions related to usage, access, interoperability, analysis, quality, validation, storage, privacy, security, and liability. Without exploring these issues, grave consequences can ensue. Some of these challenges could be elucidated from drawing on lessons from other sectors that have been immersed in using big data. For years, companies such as Google and Amazon have taken advantage of big data to tailor products to certain audiences and provide excellent customer experience. Much can be learned from these sectors that would allow the field of infectious diseases to harness big data and unlock various opportunities to enhance infectious disease research, operations, and policy.
This workshop will feature invited presentations and discussions to explore topics, including: preventing, detecting, and responding to infectious disease threats using big data and related analytics; varieties of data (e.g., demographic, geospatial, behavioral, syndromic, and laboratory) and their broader applications; means to improve their processing, utility, and validation; approaches that can be learned from other sectors to inform big data strategies for infectious disease research, operations, and policy.