The statistics of epidemic transitions

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

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Article numbere1006917
JournalPLoS computational biology
Volume15
Issue number5
DOIs
StatePublished - May 2019

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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    Drake, J. M., Brett, T. S., Chen, S., Epureanu, B. I., Ferrari, M. J., Marty, É., Miller, P. B., O’dea, E. B., O’regan, S. M., Park, A. W., & Rohani, P. (2019). The statistics of epidemic transitions. PLoS computational biology, 15(5), [e1006917]. https://doi.org/10.1371/journal.pcbi.1006917