• Mathematical model and simulations of COVID-19 2020 outbreak in New York: Predictions and implications for control measures

      Sinha, Durgesh; Tan, Peiwen; 0000-0001-7749-3710 (2020-05-01)
      The outbreak of the novel coronavirus has resulted in significant morbidity and mortality in the affected 210 countries with 2.4 million people infected and over 163 thousand deaths. The COVID-19 spike protein is effective at binding to human cells, but this COVID-19 backbone differed substantially from other, already known coronaviruses and mostly resembled viruses found in bats and pangolins. To help predict possible dynamics of COVID-19 as well as ways to contain it, this paper develops a mathematical model for the disease, which includes two different infectious routes. The model’s predictions are fitted to data from the outbreak in New York State from the first reported case from March 01, 2020 to April 19, 2020. The containment time and the severity of the outbreaks depend crucially on the contact coefficients and the isolation rate constant. When randomness is added to the model coefficients, the simulations show that the model is sensitive to the scaled contact rate among people and to the isolation rate. The model is analyzed using stability theory for ordinary differential equations and indicates that when using only isolation for control and advising self-recovery, the endemic steady state is locally stable and attractive. After reaching the peak of COVID-19 on April 14, 2020, new infections by the virus would slow down, particularly from the beginning of May at New York State if people keep the isolation. Numerical simulations with parameters estimated from New York State illustrate the analytical results and the model behavior, which may have important implications for the disease containment in other cities. Indeed, the model highlights the importance of isolation of infected individuals and advising self-recovery may be used to assess other control measures. The model is general and may be used to analyze outbreaks in other states of the United States and other countries.