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Real-time Social Internet Data to Guide Desease Forecasting Models

Banff International Research Station for Mathematical Innovation and Discovery

Globalization has created complex problems that can no longer be adequately forecasted and mitigated using traditional data analysis techniques and data sources. Disease spread is a major health concern around the world and is compounded by the increasing globalization of our society. As such, epidemiological modeling approaches need to account for rapid changes in human behavior and community perceptions. Social media has recently played a crucial role in informing and changing the response of people to the spread of infectious diseases. Recent events, such as the 2014-2015 Ebola epidemic and the 2015-2016 Zika virus epidemic, have highlighted the importance of reliable disease forecasting for decision support. This talk will discuss: 1) an operational analytic that provides global context during an unfolding outbreak and 2) a framework that combines clinical surveillance data with social Internet data and mathematical models to provide probabilistic forecasts of disease incidence and will demonstrate the value of Internet data and the real-time utility of our approach.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: Los Alamos National Laboratory

2018-04-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65693

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/