Your browser doesn't support javascript.
Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling
International Journal of Healthcare Management ; 2023.
Article in English | Scopus | ID: covidwho-2248630
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. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Keywords

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: International Journal of Healthcare Management Year: 2023 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: International Journal of Healthcare Management Year: 2023 Document Type: Article