The statistics of epidemic transitions.

• Main Authors: John M Drake, Tobias S Brett, Shiyang Chen, Bogdan I Epureanu, Matthew J Ferrari, Éric Marty, Paige B Miller, Eamon B O'Dea, Suzanne M O'Regan, Andrew W Park, Pejman Rohani
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2019-05-01
• Series: PLoS Computational Biology
• Online Access: https://doi.org/10.1371/journal.pcbi.1006917

Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.