Elementary effects analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of social distancing and mask usage

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • University of Warwick
  • The Chinese University of Hong Kong

Abstract

COVID-19 was declared a pandemic by the World Health Organisation (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for developing control strategies. This paper aims to investigate the effectiveness of masks, social distancing, lockdown and self-isolation for reducing the spread of SARS-CoV-2 infections. Our findings from an agent-based simulation modelling showed that whilst requiring a lockdown is widely believed to be the most efficient method to quickly reduce infection numbers, the practice of social distancing and the usage of surgical masks can potentially be more effective than requiring a lockdown. Our multivariate analysis of simulation results using the Morris Elementary Effects Method suggests that if a sufficient proportion of the population uses surgical masks and follows social distancing regulations, then SARS-CoV-2 infections can be controlled without requiring a lockdown.

Bibliographic note

Funding Information: FM is supported by the PathLAKE digital pathology consortium which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government's Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). Funding Information: This study was based on the findings of the lead author's dissertation project which was completed during his time at the University of Warwick, although he is now working as a Data Scientist for the Chinese University of Hong Kong. FM is supported by the PathLAKE digital pathology consortium which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government's Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). Publisher Copyright: © 2021 Elsevier Ltd

Details

Original languageEnglish
Article number104369
Number of pages13
JournalComputers in Biology and Medicine
Volume134
Early online date3 Apr 2021
Publication statusPublished - Jul 2021

Keywords

  • Agent-based modelling, Coronavirus, COVID-19, Epidemiology, Infectious diseases, Isolation, Lockdown, Masks, netlogo, Python, SARS-COV-2, Simulation, Social distancing, Stochastic processes, Stochasticity, Survival, VIRUS

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