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Title: | An approximate Bayesian approach for estimation of the instantaneous reproduction number under misreported epidemic data | Authors: | GRESSANI, Oswaldo FAES, Christel HENS, Niel |
Issue Date: | 2023 | Publisher: | WILEY | Source: | BIOMETRICAL JOURNAL, 65 (6) | Abstract: | In epidemic models, the effective reproduction number is of central importance to assess the transmission dynamics of an infectious disease and to orient health intervention strategies. Publicly shared data during an outbreak often suffers from two sources of misreporting (underreporting and delay in reporting) that should not be overlooked when estimating epidemiological parameters. The main statistical challenge in models that intrinsically account for a misreporting process lies in the joint estimation of the time-varying reproduction number and the delay/underreporting parameters. Existing Bayesian approaches typically rely on Markov chain Monte Carlo algorithms that are extremely costly from a computational perspective. We propose a much faster alternative based on Laplacian-P-splines (LPS) that combines Bayesian penalized B-splines for flexible and smooth estimation of the instantaneous reproduction number and Laplace approximations to selected posterior distributions for fast computation. Assuming a known generation interval distribution, the incidence at a given calendar time is governed by the epidemic renewal equation and the delay structure is specified through a composite link framework. Laplace approximations to the conditional posterior of the spline vector are obtained from analytical versions of the gradient and Hessian of the log-likelihood, implying a drastic speed-up in the computation of posterior estimates. Furthermore, the proposed LPS approach can be used to obtain point estimates and approximate credible intervals for the delay and reporting probabilities. Simulation of epidemics with different combinations for the underreporting rate and delay structure (one-day, two-day, and weekend delays) show that the proposed LPS methodology delivers fast and accurate estimates outperforming existing methods that do not take into account underreporting and delay patterns. Finally, LPS is illustrated in two real case studies of epidemic outbreaks. | Notes: | Gressani, O (corresponding author), Hasselt Univ, Data Sci Inst, Campus Diepenbeek,Gebouw D E125, B-3590 Agoralaan, Belgium. oswaldo.gressani@uhasselt.be |
Keywords: | Bayesian P-splinese;pidemic renewal equation;Laplace approximation;misreported data | Document URI: | http://hdl.handle.net/1942/39446 | ISSN: | 0323-3847 | e-ISSN: | 1521-4036 | DOI: | 10.1002/bimj.202200024 | ISI #: | 000913732200001 | Rights: | 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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