Samuel Scarpino

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  The Predictability Horizon for Diseases
  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, we study the intrinsic predicability of a diverse set of diseases. Instead of relying on methods which require an assumed knowledge of the data generating model, we utilize permutation entropy as a model independent metric of predicability. By studying the permutation entropy of a large collection of historical outbreaks--including, chlamydia, gonorrhea, hepatitis A, influenza, dengue, measles, polio, whooping cough, Ebola, and Zika--we identify a fundamental horizon for outbreak forecasts. Specifically, most diseases appear to be unpredictable beyond narrow time-horizons, thus highlighting the importance of dynamic modeling approaches to prediction. Our results have clear implications for the emerging field of disease forecasting and highlight the need for broader studies on the predictability of complex systems.