  During the COVID-19 pandemic, we witnessed a disproportionate infection rate
among marginalized and low-income groups. Despite empirical evidence suggesting
that structural inequalities in society contribute to health disparities, there
has been little attempt to offer a computational and theoretical explanation to
establish its plausibility and quantitative impact. This paper combines an
agent-based network model and a compartmental susceptible-infectious-removed
model (SIR) to explain how socioeconomic inequalities affect the dynamics of
disease spreading. Specifically, we focus on two aspects of structural
inequalities: wealth inequality and social segregation. Our computational model
demonstrates that under high income inequality, the infection gap widens
between the low-income and high-income groups, and also the overall infected
cases increase. We also observed that social segregation between different
socioeconomic status (SES) groups intensifies the spreading and hence mortality
rates. Furthermore, we explain the second peak of the infectious cases during a
pandemic as a result of a false sense of safety and loosening the quarantine,
among the higher SES individuals. These findings send a strong message to
policymakers; confinement measures must be accompanied by substantial financial
assistance to those from lower-income groups so that the people regardless of
their socioeconomic status can afford to stay in. Without financial assistance,
lower-income individuals will remain in circulation, which will prolong the
duration and magnitude of the infection.
