Abstract
This research examines how the transmission of SARS-CoV-2 (COVID-19) during the pandemic differs across New York City boroughs with similar population sizes but notably different average incomes. This research is important because it addresses health disparities and economic discrimination, demonstrating how mathematical models can be equipped to promote social justice. We will use Suspected - Infected - Recovered models, Euler’s Method, first and second order ordinary differential equations, and additional differential methods and equations to generate the case rates over time (January 2021-December 2022) for both the Bronx and Manhattan’s Borough UHF Community districts, then compare the data to each area’s average median income. We expect that the results of this research will demonstrate that lower-income boroughs experience higher transmission probabilities, showing that by understanding these inequalities, we may ensure that, in the future, better mathematical modeling can prevent underprivileged populations from suffering avoidable consequences of disease outbreaks.