Structural inequalities exacerbate infection disparities: A computational approach

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.