1. Bayesian nowcasting with Laplacian-P-splines.
- Author
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Sumalinab, Bryan, Gressani, Oswaldo, Hens, Niel, and Faes, Christel
- Subjects
- *
MARKOV chain Monte Carlo , *MONTE Carlo method , *COVID-19 pandemic , *COVID-19 , *EPIDEMICS - Abstract
AbstractDuring an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this paper, we present a fast and flexible Bayesian approach for nowcasting by combining P-splines and Laplace approximations. Laplacian-P-splines provide a flexible framework for nowcasting that is computationally less demanding as compared to traditional Markov chain Monte Carlo techniques. The proposed approach also permits to naturally quantify the prediction uncertainty. Model performance is assessed through simulations and the nowcasting method is applied to COVID-19 mortality and incidence cases in Belgium. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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