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Real-time updating of dynamic social networks for COVID-19 vaccination strategies

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only...

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Autores principales: Cheng, Sibo, Pain, Christopher C., Guo, Yi-Ke, Arcucci, Rossella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062280/
https://www.ncbi.nlm.nih.gov/pubmed/37360777
http://dx.doi.org/10.1007/s12652-023-04589-7
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author Cheng, Sibo
Pain, Christopher C.
Guo, Yi-Ke
Arcucci, Rossella
author_facet Cheng, Sibo
Pain, Christopher C.
Guo, Yi-Ke
Arcucci, Rossella
author_sort Cheng, Sibo
collection PubMed
description Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.
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spelling pubmed-100622802023-03-31 Real-time updating of dynamic social networks for COVID-19 vaccination strategies Cheng, Sibo Pain, Christopher C. Guo, Yi-Ke Arcucci, Rossella J Ambient Intell Humaniz Comput Original Research Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities. Springer Berlin Heidelberg 2023-03-30 /pmc/articles/PMC10062280/ /pubmed/37360777 http://dx.doi.org/10.1007/s12652-023-04589-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) .
spellingShingle Original Research
Cheng, Sibo
Pain, Christopher C.
Guo, Yi-Ke
Arcucci, Rossella
Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title_full Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title_fullStr Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title_full_unstemmed Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title_short Real-time updating of dynamic social networks for COVID-19 vaccination strategies
title_sort real-time updating of dynamic social networks for covid-19 vaccination strategies
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062280/
https://www.ncbi.nlm.nih.gov/pubmed/37360777
http://dx.doi.org/10.1007/s12652-023-04589-7
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