Evidence map›Paper›PMID 31520107›Full record

ArticleJournal of mathematical biology2019

Random fluctuations around a stable limit cycle in a stochastic system with parametric forcing.

May Anne Mata, Rebecca C Tyson, Priscilla Greenwood

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

May Anne MataDepartment of Math, Physics, and Computer Science, University of the Philippines Mindanao, Davao City, Philippines. memata@up.edu.ph.ORCID 0000-0002-2967-344X
Rebecca C TysonCMPS Department (Mathematics), Barber School of Arts and Sciences, University of British Columbia Okanagan, Kelowna, BC, Canada.ORCID 0000-0002-7373-4473
Priscilla GreenwoodDepartment of Mathematics, University of British Columbia, Vancouver, BC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many real populations exhibit stochastic behaviour that appears to have some periodicity. In terms of populations, these time series can occur as limit cycles that arise through seasonal variation of parameters such as, e.g., disease transmission rate. The general mathematical context is that of a stochastic differential system with periodic parametric forcing whose solution is a stochastically perturbed limit cycle. Earlier work identified the power spectral density (PSD) features of these fluctuations by computation of the autocorrelation function of the stochastic process and its transform. Here, we present an alternative analysis which shows that the structure of the fluctuations around the limit cycle is analogous to that of fluctuations about a fixed point. Furthermore, we show that these fluctuations can be expressed, approximately, as a factorization which reveals the combined frequencies of the limit cycle and the stochastic perturbation. This result, based on a new limit theorem near a Hopf point, yields an understanding of the previously found features of the PSD. Further insights are obtained from the corresponding stochastic equations for phase and amplitude.

Indexed as

Models, BiologicalSeasonsStochastic ProcessesAnimal DistributionAnimalsComputer SimulationDisease Transmission, InfectiousFood ChainHumansNonlinear DynamicsPredatory BehaviorDisease recurrenceEpidemiologyFloquet theoryLimit cycle with noiseOrnstein–Uhlenbeck processPeriodic forcingSeasonal forcingStochastic fluctuationsSustained oscillations

Identifiers

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.