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Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data des...

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Autores principales: Contreras, Diego Andrés, Colosi, Elisabetta, Bassignana, Giulia, Colizza, Vittoria, Barrat, Alain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214285/
https://www.ncbi.nlm.nih.gov/pubmed/35730172
http://dx.doi.org/10.1098/rsif.2022.0164
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author Contreras, Diego Andrés
Colosi, Elisabetta
Bassignana, Giulia
Colizza, Vittoria
Barrat, Alain
author_facet Contreras, Diego Andrés
Colosi, Elisabetta
Bassignana, Giulia
Colizza, Vittoria
Barrat, Alain
author_sort Contreras, Diego Andrés
collection PubMed
description Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.
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spelling pubmed-92142852022-06-22 Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases Contreras, Diego Andrés Colosi, Elisabetta Bassignana, Giulia Colizza, Vittoria Barrat, Alain J R Soc Interface Life Sciences–Physics interface Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation. The Royal Society 2022-06-22 /pmc/articles/PMC9214285/ /pubmed/35730172 http://dx.doi.org/10.1098/rsif.2022.0164 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Physics interface
Contreras, Diego Andrés
Colosi, Elisabetta
Bassignana, Giulia
Colizza, Vittoria
Barrat, Alain
Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title_full Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title_fullStr Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title_full_unstemmed Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title_short Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
title_sort impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases
topic Life Sciences–Physics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214285/
https://www.ncbi.nlm.nih.gov/pubmed/35730172
http://dx.doi.org/10.1098/rsif.2022.0164
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