Periodicity in New York State COVID‐19 Hospitalizations Leveraged From the Variable Bandpass Periodic Block Bootstrap.
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| Title: | Periodicity in New York State COVID‐19 Hospitalizations Leveraged From the Variable Bandpass Periodic Block Bootstrap. |
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| Authors: | Ahmad, Asmaa1 (AUTHOR) aahmad4@albany.edu, Valachovic, Edward1,2 (AUTHOR), Biswas, Arnab (AUTHOR) arnbiswas@wiley.com |
| Source: | Journal of Probability & Statistics. 5/20/2026, Vol. 2026, p1-10. 10p. |
| Subjects: | Statistical bootstrapping, Time series analysis, Mathematical statistics, Hospital care, Public health, COVID-19, Seasons |
| Geographic Terms: | New York (State) |
| Abstract: | The outbreak of the SARS‐CoV‐2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision‐making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patterns, is essential for forecasting future occurrences, planning interventions, and making informed decisions based on the evolution of events over time. This study employs the variable bandpass periodic block bootstrap (VBPBB) to separate and analyze different periodic components by frequency in time series data, focusing on annually correlated principal components (PCs). Bootstrapping, a method used to estimate statistical sampling distributions through random sampling with replacement, is particularly useful in this context. Specifically, block bootstrapping, a model‐independent resampling method suitable for time series data, is utilized. Its extensions are aimed at preserving the correlation structures inherent in PC processes. The VBPBB applies a bandpass filter to isolate the relevant PC frequency, thereby minimizing contamination from extraneous frequencies and noise. This approach significantly narrows the confidence intervals, enhancing the precision of estimated sampling distributions for the investigated periodic characteristics. Furthermore, we compared the outcomes of block bootstrapping for periodically correlated time series with VBPBB against those from more traditional bootstrapping methods. Our analysis shows that VBPBB provides strong evidence of the existence of an annual seasonal PC pattern in hospitalization rates not detectible by other methods, providing timing and confidence intervals for their impact. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193890418 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Periodicity in New York State COVID‐19 Hospitalizations Leveraged From the Variable Bandpass Periodic Block Bootstrap. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahmad%2C+Asmaa%22">Ahmad, Asmaa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aahmad4@albany.edu</i><br /><searchLink fieldCode="AR" term="%22Valachovic%2C+Edward%22">Valachovic, Edward</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Biswas%2C+Arnab%22">Biswas, Arnab</searchLink> (AUTHOR)<i> arnbiswas@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Probability+%26+Statistics%22">Journal of Probability & Statistics</searchLink>. 5/20/2026, Vol. 2026, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+bootstrapping%22">Statistical bootstrapping</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+care%22">Hospital care</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health%22">Public health</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Seasons%22">Seasons</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+York+%28State%29%22">New York (State)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The outbreak of the SARS‐CoV‐2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision‐making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patterns, is essential for forecasting future occurrences, planning interventions, and making informed decisions based on the evolution of events over time. This study employs the variable bandpass periodic block bootstrap (VBPBB) to separate and analyze different periodic components by frequency in time series data, focusing on annually correlated principal components (PCs). Bootstrapping, a method used to estimate statistical sampling distributions through random sampling with replacement, is particularly useful in this context. Specifically, block bootstrapping, a model‐independent resampling method suitable for time series data, is utilized. Its extensions are aimed at preserving the correlation structures inherent in PC processes. The VBPBB applies a bandpass filter to isolate the relevant PC frequency, thereby minimizing contamination from extraneous frequencies and noise. This approach significantly narrows the confidence intervals, enhancing the precision of estimated sampling distributions for the investigated periodic characteristics. Furthermore, we compared the outcomes of block bootstrapping for periodically correlated time series with VBPBB against those from more traditional bootstrapping methods. Our analysis shows that VBPBB provides strong evidence of the existence of an annual seasonal PC pattern in hospitalization rates not detectible by other methods, providing timing and confidence intervals for their impact. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/jpas/8839789 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Statistical bootstrapping Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Mathematical statistics Type: general – SubjectFull: Hospital care Type: general – SubjectFull: Public health Type: general – SubjectFull: COVID-19 Type: general – SubjectFull: Seasons Type: general – SubjectFull: New York (State) Type: general Titles: – TitleFull: Periodicity in New York State COVID‐19 Hospitalizations Leveraged From the Variable Bandpass Periodic Block Bootstrap. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahmad, Asmaa – PersonEntity: Name: NameFull: Valachovic, Edward – PersonEntity: Name: NameFull: Biswas, Arnab IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 05 Text: 5/20/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1687952X Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Probability & Statistics Type: main |
| ResultId | 1 |
