ABSTRACT
Introduction: This study aimed to develop a model utilizing the data from the top 10 countries (as of August 22, 2020) with the maximum number of infected cases. These countries are the United States of America, Brazil, India, Russia, South Africa, Peru, Mexico, Colombia, Chile, and Spain. The model is developed using the newly infected cases, new deaths, cumulative infected cases, and cumulative deaths due to COVID-19 starting from the day on which the first infected cases of COVID-19 in each of these countries is diagnosed to the date August 19, 2020. Materials and Methods: This study includes data such as the newly infected cases, new deaths, cumulative infected cases, and cumulative deaths due to COVID-19 starting from the day on which the first infected case of COVID-19 in each of these countries is diagnosed to the date August 19, 2020, in the top 10 most affected countries. The data were obtained from World Health Organization (WHO) website. To fit the data into a regression model, IBM SPSS Statistics 21.0 was used. The linear, logarithmic, quadratic, and cubic curves were fitted to the newly infected COVID-19 cases and daily deaths due to COVID-19. In choosing the best-fitted model, the coefficient of determination (R-square) was used. Results: Cubic regression model is the best fit model for new infected COVID-19 cases as well as COVID-19 deaths. It has the highest R-square value as compared to the linear, logarithmic and quadratic. Conclusion: To control the spread of infection, there is a need for aggressive control strategies from the administrative departments of all countries.