Samuel V. Scarpino, from the University of Vermont, and Giovanni Petri, from the ISI Foundation, used 25 years of Project Tycho data on eight diseases to test how well prediction models can use past epidemiologic data to predict future outbreaks.
Authors
Samuel V. Scarpino and Giovanni Petri
Related Project Tycho Datasets
United States of America - Measles
United States of America - Pertussis
United States of America - Acute nonparalytic poliomyelitis
United States of America - Acute paralytic poliomyelitis
United States of America - Acute poliomyelitis
United States of America - Influenza
United States of America - Gonorrhea
United States of America - Chlamydial Infection
United States of America - Viral hepatitis, type A
United States of America - Acute type A viral hepatitis
United States of America - Mumps
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--e.g., the expected number of cases, pace and tempo of cases needing treatment,
importation probability etc.--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--including,
gonorrhea, influenza, Zika, measles, polio, whooping cough, and mumps--we identify a
fundamental entropy barrier for 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.
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