Mathematical model to predict COVID-19 mortality rate

OBJECTIVE: Covid-19 is a highly contagious viral infection that has recently become a pandemic. Since the beginning of the pandemic, the disease has affected millions of people and taken many people's lives. The purpose of this paper is to predict and compare the number of cases and mortality rate due to Covid-19 every quarter in 2020 and 2021 in three countries: Iran, the United States, and South Korea. MATERIALS AND METHODS: The data of this study include the mortality rate of different countries of the world due to Covid-19, which has been approved by the World Health Organization (WHO). In this paper, to develop the mathematical model for mortality rate prediction, the data of the countries of Iran, the United States, and South Korea during the last two years from March 1, 2020, to March 1, 2022, have been used. In addition, the mortality trend was modeled using the MATLAB software toolbox version 2022b. During modeling, six methods including Fourier, Interpolant, Gaussian, Polynomial, Sum of Sine, and Smoothing Spline were implemented. Root Mean square error (RMSE) and final prediction error were used to evaluate the performance of these proposed methods. RESULTS: As a result of the analysis, it was shown that the Smoothing Spline model with the lowest error rate was capable of accurately evaluating and predicting Covid-19 incidence and mortality rate. Using RMSE, a prediction of the Covid-19 mortality rate for three countries is 3.76498 × 10(−5). The values of R-Square and Adj R-sq were 1 in all the experiments, which indicates the full compliance of the prediction model. CONCLUSION: Using the proposed method, the incidence rate and mortality rate can be properly assessed and compared with each other in three countries. This provides a better view of the progression of the coronavirus outbreak in spring, summer, autumn, and winter. By using the proposed method, governments will be able to prevent disease and alert people to follow health guidelines more closely, thereby reducing infection numbers and mortality rates.