The Study of Covid-19 Within the State of North Carolina in the United States

Abstract

Research on COVID-19 was done for North Carolina to assess local governmental policies, to measure COVID-19 growth rate, and to discover what predictors impact COVID-19 spread. Using a Time-Delayed SIR model, removal rates were calculated to compare how seven major metros from NC handled the pandemic. COVID-19 Data was collected from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins, and the US census was used for NC demographic data. MATLAB was used to perform multiple linear regression and solve the time-delayed Susceptible, Infected and Removed (SIR) model to evaluate local governmental policy by measuring four removal rates of each studied metro throughout the government control policies. Through the removal rate calculation, we found that upon lockdown restrictions being eased, locations near the coast suffered, despite doing well in removing infected COVID-19 persons. In the populous Charlotte-Concord-Gastonia metro, COVID-19 growth rates were higher than other metros. Nevertheless, it had the highest removal rates meaning this metro did well in handling the pandemic. Next, using multiple linear regression, the most substantial correlating factor for COVID-19 transmission was young age. The White and Black race populations were correlating factors with COVID-19 spread. Leading the age correlating predictor for the death rate in the largest metros was the 85-100 age group. In this same analysis, the 51-84 age group was found to be inversely correlated. Lastly, our education study indicates less education correlates with COVID-19 cases.

Keywords:  COVID-19, Time-Delayed SIR model, Compartmental model, Multiple linear regression, government policy, removal rates