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http://hdl.handle.net/1942/40302
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DC Field | Value | Language |
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dc.date.accessioned | 2023-06-05T11:39:09Z | - |
dc.date.available | 2023-06-05T11:39:09Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2023-06-05T08:08:01Z | - |
dc.identifier.citation | PLOS Computational Biology; Figshare. 10.1371/journal.pcbi.1010618.s001 10.1371/journal.pcbi.1010618.s002 10.1371/journal.pcbi.1010618.s003 | - |
dc.identifier.uri | http://hdl.handle.net/1942/40302 | - |
dc.description.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. | - |
dc.description.sponsorship | This project is funded by the European Union’s Research and Innovation Action (https://cordis.europa.eu/project/id/101003688) under the H2020 work programme, EpiPose grant number 101003688. | - |
dc.language.iso | en | - |
dc.publisher | PLOS Computational Biology; Figshare | - |
dc.subject.classification | Epidemiology | - |
dc.subject.other | Epidemiology | - |
dc.subject.other | Algorithms | - |
dc.subject.other | Approximation methods | - |
dc.subject.other | Pandemics | - |
dc.subject.other | Epidemiological methods and statistics | - |
dc.subject.other | Influenza | - |
dc.subject.other | Time Domain analysis | - |
dc.subject.other | Infectious disease epidemiology | - |
dc.title | EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number | - |
dc.type | Dataset | - |
local.bibliographicCitation.jcat | DS | - |
dc.description.version | 1.0 | - |
dc.rights.license | Creative Commons Attribution 4.0 International (CC-BY-4.0) | - |
dc.identifier.doi | 10.1371/journal.pcbi.1010618.s001 | - |
dc.identifier.doi | 10.1371/journal.pcbi.1010618.s002 | - |
dc.identifier.doi | 10.1371/journal.pcbi.1010618.s003 | - |
dc.description.other | Data Availability: Simulation results and real data applications in this paper can be fully reproduced with the code available on the GitHub repository https://github.com/oswaldogressani/EpiLPS-ArticleCode based on the EpiLPS package version 1.0.6 available on CRAN (https://cran.r-project.org/package=EpiLPS). | - |
local.provider.type | CrossRef | - |
local.uhasselt.international | yes | - |
local.contributor.datacreator | GRESSANI, Oswaldo | - |
local.contributor.datacreator | Wallinga, Jaco | - |
local.contributor.datacreator | Althaus, Christian L. | - |
local.contributor.datacreator | HENS, Niel | - |
local.contributor.datacreator | FAES, Christel | - |
local.contributor.datacurator | GRESSANI, Oswaldo | - |
local.contributor.rightsholder | GRESSANI, Oswaldo | - |
local.format.mimetype | - | |
local.contributororcid.datacreator | 0000-0003-4152-6159 | - |
local.contributororcid.datacreator | 0000-0003-1725-5627 | - |
local.contributororcid.datacreator | 0000-0002-5230-6760 | - |
local.contributororcid.datacreator | 0000-0003-1881-0637 | - |
local.contributororcid.datacreator | 0000-0002-1878-9869 | - |
local.contributororcid.datacurator | 0000-0003-4152-6159 | - |
local.contributororcid.rightsholder | 0000-0003-4152-6159 | - |
local.publication.doi | 10.1371/journal.pcbi.1010618 | - |
local.contributingorg.datacreator | Hasselt University | - |
local.contributingorg.datacurator | Hasselt University | - |
local.contributingorg.rightsholder | Hasselt University | - |
dc.rights.access | Open Access | - |
item.contributor | GRESSANI, Oswaldo | - |
item.contributor | Wallinga, Jaco | - |
item.contributor | Althaus, Christian L. | - |
item.contributor | HENS, Niel | - |
item.contributor | FAES, Christel | - |
item.fulltext | No Fulltext | - |
item.accessRights | Closed Access | - |
item.fullcitation | GRESSANI, Oswaldo; Wallinga, Jaco; Althaus, Christian L.; HENS, Niel & FAES, Christel (2022) EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. PLOS Computational Biology; Figshare. 10.1371/journal.pcbi.1010618.s001 10.1371/journal.pcbi.1010618.s002 10.1371/journal.pcbi.1010618.s003. | - |
crisitem.discipline.code | 03030202 | - |
crisitem.discipline.name | Epidemiology | - |
crisitem.discipline.path | Medical and health sciences > Health sciences > Public health sciences > Epidemiology | - |
crisitem.discipline.pathandcode | Medical and health sciences > Health sciences > Public health sciences > Epidemiology (03030202) | - |
crisitem.license.code | CC-BY-4.0 | - |
crisitem.license.name | Creative Commons Attribution 4.0 International (CC-BY-4.0) | - |
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