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 an integrative biological 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, the evolution of immune escape variants, 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.
Results/Conclusions
Studying the predictability of a diverse collection of historical outbreaks--including, chlamydia, gonorrhea, hepatitis A, influenza, Zika, measles, polio, whooping cough, and mumps--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, 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. Critically, we also relate the emergence of entropic barriers to the capacity of pathogen populations to adapt to novel environments, e.g., host switches, anti-microbial treatment, and immune responses. Our results highlight the importance of embracing dynamic modeling approaches to prediction and integrating key aspects of pathogen ecology and evolution with host behavior.