Effects of control measures and their importance on COVID-19 transmission dynamics

Abstract

For more than a year, governments around the world have attempted to control the COVID-19 pandemic. Control measures such as social distancing, face mask wearing, business/school closure, city or transportation lockdown, ban of mass gathering, population education and engagement, contact tracing, and improved mass testing protocols are being used to contain the pandemic. Currently, there are no studies to date that rank the importance of these measures so that the governments may allocate and target their resources towards the most effective control measures. In this paper, we propose a Discrete Time Markov Chain model that captures the above control measures and ranks them. We also show that the importance of the measures change overtime and depends on the stage of the transmission dynamics, as well as the environment. For example, contract tracing is known to be a powerful measure to effectively control the pandemic, however its influence is dynamic in nature. Our results show that contact tracing is indeed helpful during the early stage of the pandemic, but becomes less important after a vaccination program takes effect. If implemented, our novel and unique model may assist many countries in their crucial pandemic control decisions.