Evidence map›Paper›PMID 22648788›Full record

ArticleJournal of mathematical biology2013

Chaos in a seasonally perturbed SIR model: avian influenza in a seabird colony as a paradigm.

Suzanne M O'Regan, Thomas C Kelly, Andrei Korobeinikov, Michael J A O'Callaghan, Alexei V Pokrovskii, Dmitrii Rachinskii

Open access · bronzeAbstract read
In one paragraph

Article in Journal of mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.3field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 21 citations in OpenAlex.

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  8. FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19.IEEE access : practical innovations, open solutions · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Suzanne M O'ReganDepartment of Applied Mathematics, Western Gateway Building, University College Cork, Western Road, Cork, Ireland. smoregan@uga.edu
Thomas C Kelly
Andrei Korobeinikov
Michael J A O'Callaghan
Alexei V Pokrovskii
Dmitrii Rachinskii
University College Cork · IEUniversity of Limerick · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seasonality is a complex force in nature that affects multiple processes in wild animal populations. In particular, seasonal variations in demographic processes may considerably affect the persistence of a pathogen in these populations. Furthermore, it has been long observed in computer simulations that under seasonal perturbations, a host-pathogen system can exhibit complex dynamics, including the transition to chaos, as the magnitude of the seasonal perturbation increases. In this paper, we develop a seasonally perturbed Susceptible-Infected-Recovered model of avian influenza in a seabird colony. Numerical simulations of the model give rise to chaotic recurrent epidemics for parameters that reflect the ecology of avian influenza in a seabird population, thereby providing a case study for chaos in a host- pathogen system. We give a computer-assisted exposition of the existence of chaos in the model using methods that are based on the concept of topological hyperbolicity. Our approach elucidates the geometry of the chaos in the phase space of the model, thereby offering a mechanism for the persistence of the infection. Finally, the methods described in this paper may be immediately extended to other infections and hosts, including humans.

Indexed as

Nonlinear DynamicsAnimalsCharadriiformesComputer SimulationDisease OutbreaksInfluenza A Virus, H5N1 SubtypeInfluenza in BirdsSeasons

Identifiers

PMID22648788
PMCPMC7080170
OpenAlexW2065490219

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.