CSH researcher Sina Sajjadi will give a virtual talk (about research with Pourya Toranj, Basak Taraktas, Mehrzad Shadmangohar, Ulya Bayram, and Mavi Ruiz) at the Networks 2021 conference.
Official reports on the COVID-19 pandemic reveal disproportionate infection rates in lower socioeconomic groups whose economic insecurities outweigh concerns about the infection risk.
Motivated by this issue, our research introduces a model for studying the effects of income inequality on infection rates during pandemics (or public health crises).
We analyze the disparity of infection cases across different race and income groups within a context of residential segregation by constructing a novel agent based model which combines:
1. A network model for segregated communities using inter-group exposure indices (Using the official data from the city of Chicago as a case study).
2. A decision making model governing agents’ choices to go out to work vs. quarantining themselves.
3. An SIR model describing the infection spreading.
We develop both an individual-based model (ABM) and a mean-field model (ODE) and find that their results are in agreement.
We find that infection rate is directly influenced by the existing level of income. More importantly, socioeconomic inequality increases the overall infection rate of the population.
Our results convey a significant message for policy makers. Governments can slow down the spread of the infection by providing financial aids to low-income households; improving the latter’s financial security will enable them to stay in. This study offers a modeling framework to understand socioeconomic factors affecting the dynamics of infection.