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Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing

El Niño southern oscillation (ENSO) dynamics has been shown to drive seasonal influenza dynamics. Severe seasonal influenza epidemics and the 2009–2010 pandemic were coincident with chaotic regime of ENSO dynamics. ENSO dynamics from 1876 to 2016 were characterized to determine if influenza pandemic...

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Autor principal: Oluwole, Olusegun Steven Ayodele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703710/
https://www.ncbi.nlm.nih.gov/pubmed/29218303
http://dx.doi.org/10.3389/fpubh.2017.00301
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author Oluwole, Olusegun Steven Ayodele
author_facet Oluwole, Olusegun Steven Ayodele
author_sort Oluwole, Olusegun Steven Ayodele
collection PubMed
description El Niño southern oscillation (ENSO) dynamics has been shown to drive seasonal influenza dynamics. Severe seasonal influenza epidemics and the 2009–2010 pandemic were coincident with chaotic regime of ENSO dynamics. ENSO dynamics from 1876 to 2016 were characterized to determine if influenza pandemics are coupled to chaotic regimes. Time-varying spectra of southern oscillation index (SOI) and sea surface temperature (SST) were compared. SOI and SST were decomposed to components using the algorithm of noise-assisted multivariate empirical mode decomposition. The components were Hilbert transformed to generate instantaneous amplitudes and phases. The trajectories and attractors of components were characterized in polar coordinates and state space. Influenza pandemics were mapped to dynamic regimes of SOI and SST joint recurrence of annual components. State space geometry of El Niños lagged by influenza pandemics were characterized and compared with other El Niños. Timescales of SOI and SST components ranged from sub-annual to multidecadal. The trajectories of SOI and SST components and the joint recurrence of annual components were dissipative toward chaotic attractors. Periodic, quasi-periodic, and chaotic regimes were present in the recurrence of trajectories, but chaos–chaos transitions dominated. Influenza pandemics occurred during chaotic regimes of significantly low transitivity dimension (p < 0.0001). El Niños lagged by influenza pandemics had distinct state space geometry (p < 0.0001). Chaotic dynamics explains the aperiodic timing, and varying duration and strength of El Niños. Coupling of all influenza pandemics of the past 140 years to chaotic regimes of low transitivity indicate that ENSO dynamics drives influenza pandemic dynamics. Forecasts models from ENSO dynamics should compliment surveillance for novel influenza viruses.
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spelling pubmed-57037102017-12-07 Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing Oluwole, Olusegun Steven Ayodele Front Public Health Public Health El Niño southern oscillation (ENSO) dynamics has been shown to drive seasonal influenza dynamics. Severe seasonal influenza epidemics and the 2009–2010 pandemic were coincident with chaotic regime of ENSO dynamics. ENSO dynamics from 1876 to 2016 were characterized to determine if influenza pandemics are coupled to chaotic regimes. Time-varying spectra of southern oscillation index (SOI) and sea surface temperature (SST) were compared. SOI and SST were decomposed to components using the algorithm of noise-assisted multivariate empirical mode decomposition. The components were Hilbert transformed to generate instantaneous amplitudes and phases. The trajectories and attractors of components were characterized in polar coordinates and state space. Influenza pandemics were mapped to dynamic regimes of SOI and SST joint recurrence of annual components. State space geometry of El Niños lagged by influenza pandemics were characterized and compared with other El Niños. Timescales of SOI and SST components ranged from sub-annual to multidecadal. The trajectories of SOI and SST components and the joint recurrence of annual components were dissipative toward chaotic attractors. Periodic, quasi-periodic, and chaotic regimes were present in the recurrence of trajectories, but chaos–chaos transitions dominated. Influenza pandemics occurred during chaotic regimes of significantly low transitivity dimension (p < 0.0001). El Niños lagged by influenza pandemics had distinct state space geometry (p < 0.0001). Chaotic dynamics explains the aperiodic timing, and varying duration and strength of El Niños. Coupling of all influenza pandemics of the past 140 years to chaotic regimes of low transitivity indicate that ENSO dynamics drives influenza pandemic dynamics. Forecasts models from ENSO dynamics should compliment surveillance for novel influenza viruses. Frontiers Media S.A. 2017-11-23 /pmc/articles/PMC5703710/ /pubmed/29218303 http://dx.doi.org/10.3389/fpubh.2017.00301 Text en Copyright © 2017 Oluwole. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Oluwole, Olusegun Steven Ayodele
Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title_full Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title_fullStr Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title_full_unstemmed Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title_short Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing
title_sort dynamic regimes of el niño southern oscillation and influenza pandemic timing
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703710/
https://www.ncbi.nlm.nih.gov/pubmed/29218303
http://dx.doi.org/10.3389/fpubh.2017.00301
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