Exploring the Impact of Bubble Strategies on the Spread of Infectious Diseases

Abstract

The emergence of the COVID-19 pandemic has led to a great deal of scientific interest in strategies to effectively deal with epidemics. Compartmental models, such as the Susceptible-Exposed-Infectious-Removed (SEIR) model, are popular tools that allow us to study the behaviour of various epidemics. The SEIR model classifies people into groups according to their health status. However, this model assumes that the population is well-mixed which does not take into account public health policies such as social distance and bubble strategies. In this thesis we will construct the Bubble SEIR model based on the continuous-time Markov chain SEIR model. The Bubble SEIR model divides the population into subpopulations called bubbles. The model assumes that each subpopulation is well-mixed and also allows the possibility of movements between bubbles. The purpose of the thesis is studying the impact of the bubble strategies on the epidemic by simulations using the proposed model.