Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling

ABSTRACT

Businesses can play a key role in reducing exposure to COVID-19 in closed environments. This is possible by assessing the impact of Non-Pharmaceutical Interventions (NPIs) in mitigating disease exposure. This study aims to assess the impact of NPIs on COVID-19 exposure in closed environments. This is achieved by proposing an innovative COVID-19 exposure prediction framework. The developed framework consists of three modules: Agent-Based Modelling (ABM) approach, Clustering Module (CM), and Decision Tree (DT) technique. The framework also integrates these modules considering the exposure time factor to identify the level of exposure to COVID-19 in closed environments. A supermarket based in Jordan is considered a case study to test the applicability of the proposed framework in predicting exposure levels and numbers. The impact of Individual and combined NPIs application in closed environment facilities is assessed based on the exposure level and other OIs such as opening time, body temperature measurement, and the number of people inside the supermarket. Key results show that wearing Mask, Face Shield and leaving Social Distance guarantees no exposure to COVID-19 and increases the safety level to 61.9% in a closed environment such as supermarkets with a potential exposure rate of up to 28.5% if otherwise.

GRAPHICAL ABSTRACT

KEYWORDS:

• Agent-based modelling
• decision tree
• COVID-19 exposure prediction framework
• non-pharmaceutical interventions
• closed environment facilities

Acknowledgements

We want to thank the National Centre for Security and Crisis Management – Jordan for their valuable assistance in data collection and manuscript review. The sultan stores development department is also sincerely acknowledged for allowing and participating in the study.

Authors’ contributions

Ammar Al-Bazi conducted the Conceptualisation, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing – Original Draft. Faris Madi developed the software, Data Curation, Writing – Review & Editing. Anees Abu Monshar collected the resources, Writing – Review & Editing. Yousif Eliya conducted the Formal analysis, Writing – Review & Editing. Tunde Adediran and Khaled Al Khudir participated in obtaining the related resources.

Competing interests

The authors have no competing interest in relation to any part of the research, including the modelling and reporting of the results.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Ammar Al-Bazi

Ammar Al-Bazi is an Associate Professor of operations and supply chain simulation at Aston University. He holds a PhD in computer simulation and optimisation from Teesside University, UK. His research interests include hybrid simulation modelling, manufacturing and logistics simulation, metaheuristic optimisation algorithms and hybrid intelligent systems.

Faris Madi

Faris Madi is a Future Transport Cities Institute, Coventry University research assistant. He received his MBA degree in Information Technology in 2013 from Coventry University. His main interest is agent-based modelling and optimisation of complex systems. He has been involved in a KTP project to develop an autonomous scheduling system.

Anees Abu Monshar

Anees Abu Monshar is a research engineer, The Manufacturing Technology Centre (MTC). He received his MSc in Engineering Business Management from Coventry University. His research interests include agent-based modelling, logistics, transportation, and optimisation.

Yousif Eliya

Yousif Eliya graduated with a master's in health research Methodology from McMaster University, Ontario, Canada. His thesis was about Patient-Reported Outcomes in Randomized Controlled Trials of Heart Failure. He is an associate editor of the Undergraduate Research in Natural and Clinical Sciences and Technology (URNCST) Journal.

Tunde Adediran

Tunde Adediran is a Lecturer in Project Management at the School of Strategy and Leadership, Coventry University. He holds a PhD in computer simulation from Coventry University, UK. His research interests include agent-based modelling and heuristics optimisation.

Khaled Al Khudir

Khaled Al Khudir is a lecturer at the mechanical engineering school, engineering faculty, environment, and computing at Coventry University. He obtained a PhD in Automatic Control, Bioengineering, and Operations Research from Sapienza University of Rome (Italy). His research interests include areas of robotics and control.