Using the Lagrange Interpolation Polynomial Method to Calculate the Prevalence of Coronavirus Disease 2019 (COVID-19) in Turkey

The goal of this paper is to look into a numerical approximation for the spread of the coronavirus disease 2019 (COVID-19) in Turkey. From March 11th to November 30th, all data is examined one by one for this purpose. Under Turkey's influence, Covid-19 displayed a widespread, as did the rest of the world shortly after. The information revealed daily about the disease must be formulated mathematically. The time -dependent variation of every piece of data has a significant place in Lagrange interpolation. Using this technique, data were obtained as polynomials. These polynomials were used to draw the simulation of the spread and death rate in turkey by the virus and to track the increase and decrease over the months. The Lagrange interpolation method does not require evenly spaced x values. On the other hand, usually preferable to find the closest value in the table and then use the lowest -order interpolation that is consistent with the data's functional form. A function for monthly and overall data on the number of COVID-19 disease deaths and cases infected with the disease can be obtained using this technique. Simulations for month -by -month and general data are obtained using Lagrange interpolation polynomial. The rate of spread of disease and death numbers is obtained by taking the first derivative of this function. An analysis table for the given data is presented depending on the average speed of death and spread of disease monthly. The information in these simulations is used to determine the disease's peak point and different change values. The rates of death and disease spread by month are compared using these simulations. As a result of this comparison, it can be seen in which months the rate of spread of disease and death increases and decreases. Monthly increase and decrease values can be seen in Figure 3 and Figure 6. For example, a relative decrease can be observed in April and May. Finally, it can be concluded that the Lagrange interpolation polynomial method offered a mathematical framework for data analysis.