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The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions
The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new C...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107333/ https://www.ncbi.nlm.nih.gov/pubmed/35601479 http://dx.doi.org/10.1016/j.patcog.2022.108790 |
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author | Novakovic, Aleksandar Marshall, Adele H. |
author_facet | Novakovic, Aleksandar Marshall, Adele H. |
author_sort | Novakovic, Aleksandar |
collection | PubMed |
description | The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population. |
format | Online Article Text |
id | pubmed-9107333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91073332022-05-16 The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions Novakovic, Aleksandar Marshall, Adele H. Pattern Recognit Article The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population. Published by Elsevier Ltd. 2022-10 2022-05-14 /pmc/articles/PMC9107333/ /pubmed/35601479 http://dx.doi.org/10.1016/j.patcog.2022.108790 Text en Crown Copyright © 2022 Published by Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Novakovic, Aleksandar Marshall, Adele H. The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title | The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title_full | The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title_fullStr | The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title_full_unstemmed | The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title_short | The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
title_sort | cp‐abm approach for modelling covid‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107333/ https://www.ncbi.nlm.nih.gov/pubmed/35601479 http://dx.doi.org/10.1016/j.patcog.2022.108790 |
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