Title: On the predictability of infectious disease outbreaks,
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, demand for prophylactic
equipment, 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, chlamydia, dengue,
gonorrhea, hepatitis A, influenza, measles, mumps, polio, and whooping
cough--we identify a fundamental entropy barrier for infectious disease time
series forecasting. However, we find that for most diseases this barrier to
prediction is often well beyond the time scale of single outbreaks. 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 across contagions. Our results
highlight the importance of moving beyond time series forecasting, by embracing
dynamic modeling approaches, and suggest challenges for performing model
selection across long 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.