A general computational framework for COVID-19 modelling with applications to testing varied interventions in education environments

Moore, Joshua W., Lau, Zechariah, Kaouri, Katerina ORCID: https://orcid.org/0000-0002-9850-253X, Dale, Trevor C. ORCID: https://orcid.org/0000-0002-4880-9963 and Woolley, Thomas E. ORCID: https://orcid.org/0000-0001-6225-5365 2021. A general computational framework for COVID-19 modelling with applications to testing varied interventions in education environments. COVID 1 (4) , pp. 674-703. 10.3390/covid1040055

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Abstract

We construct a spatially-compartmental, individual-based model of the spread of SARS-CoV-2 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and to Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in ‘test-to-release’ scenarios in which individuals who have been exposed to infection are released from isolation after a negative LFD result. LFDs are also compared to other NPIs; we find that, although LFD testing can be used to mitigate the spread of SARS-CoV-2, it is more effective to invest in personal protective equipment, e.g., masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data becomes available.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics Biosciences European Cancer Stem Cell Research Institute (ECSCRI)
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Publisher:	MDPI
ISSN:	2673-8112
Date of First Compliant Deposit:	26 November 2021
Date of Acceptance:	23 November 2021
Last Modified:	09 Nov 2023 18:59
URI:	https://orca.cardiff.ac.uk/id/eprint/145736

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