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estimateR: an R package to estimate and monitor the effective reproductive number

BACKGROUND: Accurate estimation of the effective reproductive number ([Formula: see text] ) 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 observatio...

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Autores principales: Scire, Jérémie, Huisman, Jana S., Grosu, Ana, Angst, Daniel C., Lison, Adrian, Li, Jinzhou, Maathuis, Marloes H., Bonhoeffer, Sebastian, Stadler, Tanja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416499/
https://www.ncbi.nlm.nih.gov/pubmed/37568078
http://dx.doi.org/10.1186/s12859-023-05428-4
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author Scire, Jérémie
Huisman, Jana S.
Grosu, Ana
Angst, Daniel C.
Lison, Adrian
Li, Jinzhou
Maathuis, Marloes H.
Bonhoeffer, Sebastian
Stadler, Tanja
author_facet Scire, Jérémie
Huisman, Jana S.
Grosu, Ana
Angst, Daniel C.
Lison, Adrian
Li, Jinzhou
Maathuis, Marloes H.
Bonhoeffer, Sebastian
Stadler, Tanja
author_sort Scire, Jérémie
collection PubMed
description BACKGROUND: Accurate estimation of the effective reproductive number ([Formula: see text] ) 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 [Formula: see text] 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 [Formula: see text] . SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05428-4.
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spelling pubmed-104164992023-08-12 estimateR: an R package to estimate and monitor the effective reproductive number Scire, Jérémie Huisman, Jana S. Grosu, Ana Angst, Daniel C. Lison, Adrian Li, Jinzhou Maathuis, Marloes H. Bonhoeffer, Sebastian Stadler, Tanja BMC Bioinformatics Software BACKGROUND: Accurate estimation of the effective reproductive number ([Formula: see text] ) 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 [Formula: see text] 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 [Formula: see text] . SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05428-4. BioMed Central 2023-08-11 /pmc/articles/PMC10416499/ /pubmed/37568078 http://dx.doi.org/10.1186/s12859-023-05428-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Scire, Jérémie
Huisman, Jana S.
Grosu, Ana
Angst, Daniel C.
Lison, Adrian
Li, Jinzhou
Maathuis, Marloes H.
Bonhoeffer, Sebastian
Stadler, Tanja
estimateR: an R package to estimate and monitor the effective reproductive number
title estimateR: an R package to estimate and monitor the effective reproductive number
title_full estimateR: an R package to estimate and monitor the effective reproductive number
title_fullStr estimateR: an R package to estimate and monitor the effective reproductive number
title_full_unstemmed estimateR: an R package to estimate and monitor the effective reproductive number
title_short estimateR: an R package to estimate and monitor the effective reproductive number
title_sort estimater: an r package to estimate and monitor the effective reproductive number
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416499/
https://www.ncbi.nlm.nih.gov/pubmed/37568078
http://dx.doi.org/10.1186/s12859-023-05428-4
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