On the unpredictability of outbreaks

Wednesday, June 8, 2016

12:00 p.m.

ISI Red Room

Samuel Scarpino

Infectious disease outbreaks recapitulate biology, emerging from the multi-level interaction of hosts, pathogens, and their shared environment.  Therefore, predicting when and where diseases will spread requires a complex systems approach to modeling.  However, it remains to be demonstrated that such complex systems are fundamentally predictable.  To investigate this question, I study the intrinsic predicability of a diverse set of diseases.  However, instead of relying on methods which require an assumed knowledge of the data generating model, I utilize permutation entropy as a model independent metric of predicability.  By studying the permutation entropy of a large collection of historical outbreaks--including, influenza, dengue, measles, polio, whooping cough,  Ebola, and Zika--I identify fundamental limits to our ability to forecast outbreaks.  Specifically, most diseases appear to be unpredictable beyond narrow time-horizons.  These results have clear implications for the emerging field of disease forecasting and highlight the need for broader studies on the predictability of complex systems.