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Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method

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  • Oliver Stoner
  • Alba Halliday
  • Theo Economou
Abstract
The COVID‐19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision‐making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID‐19 and other diseases, and critically evaluate current state‐of‐the‐art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID‐19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision‐making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15‐month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID‐19 and future epidemics.

Suggested Citation

  • Oliver Stoner & Alba Halliday & Theo Economou, 2023. "Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method," Biometrics, The International Biometric Society, vol. 79(3), pages 2537-2550, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2537-2550
    DOI: 10.1111/biom.13810
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    References listed on IDEAS

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    4. Michael Höhle & Matthias an der Heiden, 2014. "Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011," Biometrics, The International Biometric Society, vol. 70(4), pages 993-1002, December.
    5. Sarah F McGough & Michael A Johansson & Marc Lipsitch & Nicolas A Menzies, 2020. "Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-20, April.
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