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PLoS Comput Biol. 2019 May 8;15(5):e1006917. doi: 10.1371/journal.pcbi.1006917. eCollection 2019 May.

The statistics of epidemic transitions.

Author information

1
Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America.
2
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America.
3
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
4
Automotive Research Center, University of Michigan, Ann Arbor, Michigan, United States of America.
5
Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America.
6
Department of Mathematics, North Carolina A&T State University, Greensboro, North Carolina, United States of America.

Abstract

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

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