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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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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. |
format | Online Article Text |
id | pubmed-9214285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
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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