Modeling COVID-19: Forecasting and analyzing the dynamics of the outbreak in Hubei and Turkey

ibrahim Halil Aslan; Mahir Demir; Michael Morgan Wise; Suzanne Lenhart

A deterministic compartmental model has been developed by using ordinary differential equations (ODEs) to understand the dynamics of COVID-19 in Hubei, China (Chubb & Jacobsen, 2010; Keeling & Rohani, 2008; Kot, 2001 ). In the model, the total population N (t) at time t is divided into the following six compartments: susceptible S(t), susceptible 3 in quarantine (isolated class) S q (t), exposed E(t), infected (asymptomatic or having mild symptoms) I(t), reported (infected) cases (hospitalized if get severe symptoms or quarantined if get mild symptoms) I q (t), and recovered R(t). Note that all individuals who, upon testing, test positive are immediately isolated. The transition flows among compartments are given in Figure 1 . The rate of reported cases i q denotes the number of individuals who transition from the infected class I to the reported class I q per day; it is also directly related to the daily number of COVID-19 tests carried out during the outbreak. Figure 1 : Flow diagram illustrating the disease transitions among the compartments Susceptible individuals make the transition to the S q (t) compartment with a rate of