Analysis coronavirus disease (COVID-19) model using numerical approaches and logistic model

The coronavirus disease (COVID-19) is a global health care problem that international efforts have been suggested and discussed to control this disease. Although, there are many researches have been conducted on the basis of the clinical data and recorded infected cases, there is still scope for further research due to the fact that a number of complicated parameters are involved for future prediction. Thus, mathematical modeling with computational simulations is an important tool that estimates key transmission parameters and predicts model dynamics of the disease. In this paper, we review and introduce some models for the COVID-19 that can address important questions about the global health care and suggest important notes. We suggest three well known numerical techniques for solving such equations, they are Euler's method, Runge-Kutta method of order two (RK2) and of order four (RK4). Results based on the suggested numerical techniques and providing approximate solutions give important key answers to this global issue. Numerical results may use to estimate the number susceptible, infected, recovered and quarantined individuals in the future. The results here may also help international efforts for more preventions and improvement their intervention programs. More interestedly, for both countries, Turkey and Iraq, the basic reproduction numbers R-0 have been reported recently by several groups, a research estimation by 9 April 2020 revealed that R-0 for Turkey is 7.4 and for Iraq is 3.4, which are noticeably increased from the beginning of the pandemic. In addition, on the basis of WHO situation reports, the new confirmed cases in Turkey on 11 April are 5138, and in Iraq on 29 May are 416, which can be counted as the peak value from the beginning of the disease. Thus, we investigate the forecasting epidemic size for Turkey and Iraq using the logistic model. It can be concluded that the suggested model is a reasonable description of this epidemic disease.