Estimating Trends in COVID-19 Infected Cases Based on Panel Data Regression Modelling

A. Rajarathinam and P. Tamilselvan

Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable, thus, the world encountered health crises and substantial economic crises. This study analysed the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins, published by the Health & Family Welfare Department, Government of Tamil Nadu, India. Panel data regression models were employed to study the trends. EViews Ver.11. software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. Very fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanyakumari districts. A random-effects model was found to be an appropriate model to study the trend. Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random-effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October, 2020.

View Fulltext Back