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Multivariate hierarchical frameworks for modeling delayed reporting in count data

Author

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  • Oliver Stoner
  • Theo Economou
Abstract
In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short‐term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. We discuss previous approaches to modeling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modeled simultaneously in a flexible way. This framework can also be easily adapted to allow for the presence of underreporting in the final observed count. To illustrate our approach and to compare it with existing frameworks, we present a case study of reported dengue fever cases in Rio de Janeiro. Based on both within‐sample and out‐of‐sample posterior predictive model checking and arguments of interpretability, adaptability, and computational efficiency, we discuss the relative merits of different approaches.

Suggested Citation

  • Oliver Stoner & Theo Economou, 2020. "Multivariate hierarchical frameworks for modeling delayed reporting in count data," Biometrics, The International Biometric Society, vol. 76(3), pages 789-798, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:789-798
    DOI: 10.1111/biom.13188
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    References listed on IDEAS

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    1. 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.
    2. Shaun R. Seaman & Pantelis Samartsidis & Meaghan Kall & Daniela De Angelis, 2022. "Nowcasting COVID‐19 deaths in England by age and region," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1266-1281, November.
    3. Radka Jersakova & James Lomax & James Hetherington & Brieuc Lehmann & George Nicholson & Mark Briers & Chris Holmes, 2022. "Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 834-860, August.

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