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dc.contributor.author
Scire, Jérémie
dc.contributor.author
Huisman, Jana S.
dc.contributor.author
Grosu, Ana
dc.contributor.author
Angst, Daniel C.
dc.contributor.author
Lison, Adrian
dc.contributor.author
Li, Jinzhou
dc.contributor.author
Maathuis, Marloes H.
dc.contributor.author
Bonhoeffer, Sebastian
dc.contributor.author
Stadler, Tanja
dc.date.accessioned
2023-08-30T08:53:33Z
dc.date.available
2023-08-26T03:20:40Z
dc.date.available
2023-08-30T08:53:33Z
dc.date.issued
2023-08-11
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/s12859-023-05428-4
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/628320
dc.identifier.doi
10.3929/ethz-b-000628320
dc.description.abstract
Background: Accurate estimation of the effective reproductive number (Re) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the Re estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. Results: The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. Conclusions: The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of Re .
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
R package
en_US
dc.subject
Epidemiology
en_US
dc.subject
Effective reproductive number
en_US
dc.subject
Re
en_US
dc.subject
Rt
en_US
dc.subject
Surveillance
en_US
dc.subject
Monitoring
en_US
dc.subject
Outbreak
en_US
dc.subject
COVID-19
en_US
dc.title
estimateR: an R package to estimate and monitor the effective reproductive number
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
24
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
310
en_US
ethz.size
26 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Real-time monitoring of COVID-19 transmission through phylodynamics
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
ethz.grant.agreementno
196267
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Special Call on Coronaviruses
ethz.date.deposited
2023-08-26T03:20:40Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-08-30T08:53:34Z
ethz.rosetta.lastUpdated
2024-02-03T02:58:15Z
ethz.rosetta.versionExported
true
ethz.COinS
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