The statistics of epidemic transitions.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
entrez:
9
5
2019
pubmed:
9
5
2019
medline:
16
11
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31067217
doi: 10.1371/journal.pcbi.1006917
pii: PCOMPBIOL-D-18-00887
pmc: PMC6505855
doi:
Types de publication
Editorial
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1006917Subventions
Organisme : NIGMS NIH HHS
ID : U01 GM110744
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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