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A spline-based time-varying reproduction number for modelling epidemiological outbreaks

Author

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  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

We develop in this manuscript a method for performing estimation and inference for the reproduction number of an epidemiological outbreak. The estimator is time-dependent and uses spline modeling to adapt to changes in the outbreak. This is accomplished by directly modeling the series of new infections as a function of time and subsequently using the derivative of the function to define a time-varying reproduction number.

Suggested Citation

  • Pircalabelu, Eugen, 2021. "A spline-based time-varying reproduction number for modelling epidemiological outbreaks," LIDAM Discussion Papers ISBA 2021030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2021030
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    References listed on IDEAS

    as
    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    2. Shinsuke Koyama & Taiki Horie & Shigeru Shinomoto, 2021. "Estimating the time-varying reproduction number of COVID-19 with a state-space method," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-18, January.
    3. Konstantinos Fokianos & Roland Fried, 2010. "Interventions in INGARCH processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(3), pages 210-225, May.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    5. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    reproduction number; time dependency; spline modeling; COVID-19;
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