Effect of control measure on the development of new COVID-19 cases through SIR model simulation

Abstract In December 2019, the outbreak of corona virus disease, also known as COVID-19, was first reported in Wuhan, China [1]. Within only one month, the disease quickly spread to the United States through the transmission of respiratory droplets released when an infected individual sneezes or coughs [2]. Throughout the course of 9 months, the US reported over 8 million cases and 204,000 deaths, affecting the daily lives of American citizens [3]. As trends in corona virus cases are changing daily, it’s important to monitor these trends and observe the causes for the increase and decrease in new cases. The trends in new corona virus cases in the US as well as New Jersey are simulated using a modified Susceptible-Infected-Recovered (SIR) model. The new case graphs from the simulations reflect the new case trends in both the US and New Jersey and can be used to understand the mechanism behind the rates of corona virus infection as well as predict future corona virus trends. Comparisons between the results of the simulations and observed data show the effectiveness of control measures such as quarantine, physical distancing, and wearing masks. The extended time period of control measures taken in New Jersey led to a gradual decline in new cases reported daily while the US new cases showed a second wave of growth after control measures were implemented to a lesser extent.