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On realized serial and generation intervals given control measures: The COVID-19 pandemic case

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequent...

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Autores principales: Torneri, Andrea, Libin, Pieter, Scalia Tomba, Gianpaolo, Faes, Christel, Wood, James G., Hens, Niel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031880/
https://www.ncbi.nlm.nih.gov/pubmed/33780436
http://dx.doi.org/10.1371/journal.pcbi.1008892
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author Torneri, Andrea
Libin, Pieter
Scalia Tomba, Gianpaolo
Faes, Christel
Wood, James G.
Hens, Niel
author_facet Torneri, Andrea
Libin, Pieter
Scalia Tomba, Gianpaolo
Faes, Christel
Wood, James G.
Hens, Niel
author_sort Torneri, Andrea
collection PubMed
description The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.
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spelling pubmed-80318802021-04-15 On realized serial and generation intervals given control measures: The COVID-19 pandemic case Torneri, Andrea Libin, Pieter Scalia Tomba, Gianpaolo Faes, Christel Wood, James G. Hens, Niel PLoS Comput Biol Research Article The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof. Public Library of Science 2021-03-29 /pmc/articles/PMC8031880/ /pubmed/33780436 http://dx.doi.org/10.1371/journal.pcbi.1008892 Text en © 2021 Torneri et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Torneri, Andrea
Libin, Pieter
Scalia Tomba, Gianpaolo
Faes, Christel
Wood, James G.
Hens, Niel
On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title_full On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title_fullStr On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title_full_unstemmed On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title_short On realized serial and generation intervals given control measures: The COVID-19 pandemic case
title_sort on realized serial and generation intervals given control measures: the covid-19 pandemic case
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031880/
https://www.ncbi.nlm.nih.gov/pubmed/33780436
http://dx.doi.org/10.1371/journal.pcbi.1008892
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