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Simultaneous Confidence Band Approach for Comparison of COVID-19 Case Counts

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  • Q. Shao

    (The University of Toledo)

Abstract

The outbreak of the novel coronavirus (COVID-19) was declared to be a global emergency in January of 2020, and everyday life throughout the world was disrupted. Among many questions about COVID-19 that remain unanswered, it is of interest for society to identify whether there is any significant difference in daily case counts between males and females. The daily case count sequences are correlated due to the nature of a contagious disease, and contain a nonlinear trend owing to several unexpected events, such as vaccinations and the appearance of the delta variant. It is possible that these unexpected events have changed the dynamical system that generates data. The classic t-test is not appropriate to analyze such correlated data with a nonconstant trend. This study applies a simultaneous confidence band approach in an attempt to overcome these difficulties; that is, a simultaneous confidence band for the trend of an autoregressive moving-average time series is constructed using B-spline estimation. The proposed method is applied to the daily case count data of seniors of both genders (at least 60 years old) in the State of Ohio from April 1, 2020 to March 31, 2022, and the result shows that there is a significant difference at the 95% confidence level between the two gender case counts adjusted for the population sizes.

Suggested Citation

  • Q. Shao, 2023. "Simultaneous Confidence Band Approach for Comparison of COVID-19 Case Counts," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 372-383, July.
    • Handle: RePEc:spr:stabio:v:15:y:2023:i:2:d:10.1007_s12561-023-09364-y
    DOI: 10.1007/s12561-023-09364-y
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    References listed on IDEAS

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    1. Gang Liu & Qin Shao & Robert Lund & Jonathan Woody, 2016. "Testing for seasonal means in time series data," Environmetrics, John Wiley & Sons, Ltd., vol. 27(4), pages 198-211, June.
    2. Qin Shao & Lijian Yang, 2017. "Oracally efficient estimation and consistent model selection for auto-regressive moving average time series with trend," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 507-524, March.
    3. Peter Hall & Ingrid Van Keilegom, 2003. "Using difference‐based methods for inference in nonparametric regression with time series errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 443-456, May.
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