Characterizing the epidemiological dynamics of COVID-19 using a non-parametric framework

The recently evolved family of coronaviruses known as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) or COVID-19 has spread across the world at a critically alarming rate, thereby causing a global health emergency. Several nations have been adversely affected in terms of both social and economic aspects. Hence there is an utmost need to control the transmission rate of the virus by incorporating stringent control measures. In this article, a non-parametric framework for characterizing the epidemiological behaviour of COVID-19 is suggested. Several statistical analysis tests have been conducted on the time series data acquired for four countries to derive a relationship between the three considered cases, viz. the new incidence of COVID-19, new deaths, and new sample testing facilities. Further, considering the dynamical behaviour of the sample data, a smoothing spline approach is implemented to obtain a better analysis of the observations. Subsequently, autocorrelation function is used to study the degree of correlation for each considered case for specific time lags. Finally, the non-parametric kernel density estimate is adopted for obtaining a robust characterization of the underlying distributions pertaining to each case considered in this study. Hence these observations lead to the development of an efficient prediction framework that can be implemented for analysing the epidemiological behaviour of the COVID-19 pandemic.