Elementary Effects Analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of Social Distancing and Mask Usage

TL;DR

This study uses agent-based simulation to analyze how social distancing and mask usage significantly influence COVID-19 infection rates, potentially reducing the need for lockdowns.

Contribution

It introduces an elementary effects analysis to quantify the impact of behavioral factors on infection spread in a simulation model.

Findings

Social distancing and mask usage can control infections without lockdowns.

Lockdowns are less effective than behavioral interventions in the model.

Multivariate analysis highlights key parameters influencing infection control.

Abstract

COVID-19 was declared a pandemic by the World Health Organization (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 effective policy making. 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 based on agent-based simulation modelling show 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 wore surgical masks and followed social distancing regulations, then SARS-CoV-2 infections can be controlled without requiring a lockdown.