On the Predictability of Infectious Disease Outbreaks

Samuel ScarpinoWednesdays@NICO | 12:00-1:00 PM, May 24, 2017 | Chambers Hall, Lower Level

Samuel Scarpino - Assistant Professor of Mathematics & Statistics, University of Vermont

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Abstract:

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and their shared environment.  As a result, predicting when, where, and how far diseases will spread requires a complex systems approach to modeling.  Recent studies have demonstrated that predicting different components of outbreaks is feasible.  Therefore, advancing both the science and practice of disease forecasting now requires testing for the presence of fundamental limits to outbreak prediction.  To investigate the question of outbreak prediction, we study the information theoretic limits to forecasting across a broad set of infectious diseases using permutation entropy as a model independent measure of predictability.  Studying the predictability of a diverse collection of historical outbreaks we identify a fundamental entropy barrier for infectious disease time series forecasting.  However, we find that for most diseases this barrier to prediction is often well beyond the time scale of single outbreaks, implying prediction is likely to succeed.  We also find that the forecast horizon varies by disease and demonstrate that both shifting model structures and social network heterogeneity are the most likely mechanisms for the observed differences in predictability across contagions.  Our results highlight the importance of moving beyond time series forecasting, by embracing dynamic modeling approaches to prediction and suggest challenges for performing model selection across long disease time series.  We further anticipate that our findings will contribute to the rapidly growing field of epidemiological forecasting and may relate more broadly to the predictability of complex adaptive systems.

Further Reading:

This talk is based on the following paper: On the predictability of infectious disease outbreaks, arXiv:1703.07317 [physics.soc-ph]

Bio:

Samuel V. Scarpino is a biologist investigating questions at the intersection of biology, behavior, and disease. To date, Scarpino has published twenty-five peer-reviewed articles, given over 100 presentations at national and international conferences, and is a Deputy Editor at PLoS Neglected Tropical Diseases. His publications on Ebola, whooping cough, and influenza have been covered by the New York Times, NPR, the Economist, Smithsonian Magazine, and numerous other national and international venues. Sam is currently an Assistant Professor of Mathematics & Statistics and is a core faculty member in the Complex Systems Center at the University of Vermont.  He earned a B.Sc. in biology from Indiana University Bloomington in 2007 and a Ph.D. in integrative biology from The University of Texas at Austin in 2013.  He attended the Santa Fe Institute's Complex Systems Summer School in 2010 and was an SFI Omidyar Postdoctoral Fellow from 2013 - 2016.

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