Samuel V. Scarpino from Northeastern University and Giovanni Petri from the ISI Foundation used Project Tycho data to study the predictability of a diverse collection of outbreaks. The results demonstrate that outbreaks should be predictable but that accurate long-range forecasts for complex adaptive systems, e.g., contagions beyond a single outbreak, may be impossible to achieve due to the emergence of entropy barriers.
Related Project Tycho Datasets
United States of America - Acute nonparalytic poliomyelitis
United States of America - Acute paralytic poliomyelitis United States of America - Acute poliomyelitis United States of America - Acute type A viral hepatitis
United States of America - Chlamydia trachomatis infection
United States of America - Chlamydial infection
United States of America - Gonorrhea
United States of America - Influenza
United States of America - Measles
United States of America - Mumps United States of America - Pertussis United States of America - Viral hepatitis, type A
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.
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