The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions

Aleksandar Novakovic; Adele H. Marshall

doi:10.1016/j.patcog.2022.108790

The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions

Aleksandar Novakovic^*, Adele H. Marshall^*

^*Corresponding author for this work

School of Mathematics and Physics

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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Abstract

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.

Original language	English
Article number	108790
Number of pages	14
Journal	Pattern Recognition
Volume	130
Early online date	27 May 2022
DOIs	https://doi.org/10.1016/j.patcog.2022.108790
Publication status	Published - Oct 2022

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10.1016/j.patcog.2022.108790Licence: CC BY-NC-ND

The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions
2022 Crown Copyright. This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Final published version, 2.18 MBLicence: CC BY-NC-ND

Cite this

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The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions

Abstract

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