Date
Wednesday, June 8, 2016
Time
12:00 p.m.
Location
ISI Red Room
Speaker(s)
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.