A Mathematical Model to Forecast and Compare COVID-19 Outbreak in Male and Female using Polynomial Regression Analysis - Bhattacharya, A; Dutta, P; Halder, S; Banerjee, P; Bandyopadhyay, SK (International Journal of Computer Sciences and Engineering, 2020)
COVID-19, a fast-spreading infectious disease, has taken away the life of a large number of people miserably with its dreadful consequences globally. The situation demands immediate controlling measures to stop the quick spread of disease. Governments and other legislative bodies rely on insights from different predictions to suggest and enforce relevant steps. Forecasting, however, requires sufficient authentic historical data. Due to deficiency of important authentic data, standard models have shown comparatively lower accuracy for prediction. Among the standard models for COVID19 global pandemic prediction, statistical models have received more attention by authorities. So, far the predictions from all the standard models gave insights that are irrespective of gender. In this study we proposed a statistical model that uses regression analysis techniques to predict the rate of growth of Covid-19 infections in males and females based on their population density in each country. Finally, we used a dataset of COVID-19 patients to evaluate the accuracy of the proposed model.