Elementary effects analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of social distancing and mask usage.
subject: epidemiology, coronavirus, infectious diseases, isolation, virus, covid-19, SARS-CoV-2, simulation, social distancing, survival, lockdown, Masks, agent-based modelling, NetLogo, stochastic processes, Python, Stochasticity
description: 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.
creator: Kelvin K F Li, Stephen A Jarvis, Fayyaz Minhas
source: Computers in biology and medicine
date: 2021