Detecting frequency modulation in stochastic time-series data

Hauber, Adrian L.; Sigloch, Christian; Timmer, Jens

doi:10.1103/physreve.106.024204

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Let us consider the q n-dimensional vectors y j made from these observations and calculate the combinations of the y j 's, which are orthogonal and have a variance decreasing with i. They constitute a matrix denoted as Y and defined as follows: ∀i = 1, n, ∑ q j=1 y ji a ji = Ya i or < y i , a i >= Ya i (9) and var…”

Section: Principal Component Analysismentioning

confidence: 99%

“…This objective is close to that of the stationarity rupture tests studied for about forty years by statisticians. Indeed, since the seminal work by J. Deshayes and D. Picard on the stationarity rupture in time series [ 3 , 4 ], many works have dealt with stationarity breaking [ 5 , 6 , 7 , 8 , 9 , 10 ], the most recent using the concept of functional statistics, which considers observed curves of incidence or mortality as functions to be estimated in parametrized sets of functions [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Waku,

Oshinubi,

Adam

et al. 2023

Diseases

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Objective: The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. Methods: Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. Results: This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. Conclusion: This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.

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Section: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Waku,

Oshinubi,

Adam

et al. 2023

Diseases

View full text Add to dashboard Cite

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Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19

Demongeot,

Magal,

Oshinubi

2024

Mathematical Medicine and Biology: A Journal of the IMA

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Background Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. Methods Indicators for detecting the transition endemic/epidemic, with four scalars to be compared, are calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the principal component analysis (PCA), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). Results A score has been built from the four proposed indicators using the PCA, which allows an acceptable level of forecasting performance by giving a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of the limits of the different phases of the COVID-19 outbreak in successive endemic/epidemic transitions for three countries, France, India, and Japan. Conclusion We provided a new forecasting method for predicting an epidemic wave occurring after an endemic phase for a contagious disease.

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Detecting frequency modulation in stochastic time-series data

Cited by 2 publications

References 21 publications

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19

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