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Title: | EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number | Data Creator - person: | GRESSANI, Oswaldo Wallinga, Jaco Althaus, Christian L. HENS, Niel FAES, Christel |
Data Creator - organization: | Hasselt University | Data Curator - person: | GRESSANI, Oswaldo | Data Curator - organization: | Hasselt University | Rights Holder - person: | GRESSANI, Oswaldo | Rights Holder - organization: | Hasselt University | Publisher: | PLOS Computational Biology; Figshare | Issue Date: | 2022 | Abstract: | In infectious disease epidemiology, the instantaneous reproduction number is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in’’ estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France. | Research Discipline: | Medical and health sciences > Health sciences > Public health sciences > Epidemiology (03030202) | Keywords: | Epidemiology;Algorithms;Approximation methods;Pandemics;Epidemiological methods and statistics;Influenza;Time Domain analysis;Infectious disease epidemiology | DOI: | 10.1371/journal.pcbi.1010618.s001 10.1371/journal.pcbi.1010618.s002 10.1371/journal.pcbi.1010618.s003 |
Source: | PLOS Computational Biology; Figshare. 10.1371/journal.pcbi.1010618.s001 10.1371/journal.pcbi.1010618.s002 10.1371/journal.pcbi.1010618.s003 | Publications related to the dataset: | 10.1371/journal.pcbi.1010618 | License: | Creative Commons Attribution 4.0 International (CC-BY-4.0) | Access Rights: | Open Access | Version: | 1.0 | Category: | DS | Type: | Dataset |
Appears in Collections: | Datasets |
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