Analyzing the Data of COVID-19 with Quasi-Distribution Fitting Based on Piecewise B-Spline Curves

Abstract

Facing the worldwide coronavirus disease 2019 (COVID-19) pandemic, a new fitting method (QDF, quasi-distribution fitting) which can be used to analyze the data of COVID-19 is developed based on piecewise quasi-uniform B-spline curves. For any given country or district, it simulates the distribution histogram data which is made from the daily confirmed cases (or the other data including daily recovery cases and daily fatality cases) of COVID-19 with piecewise quasi-uniform B-spline curves. After using the area normalization method, the fitting curves could be regarded as a kind of probability density function (PDF): its mathematical expectation and the variance could be used to analyze the situation of the coronavirus pandemic. Numerical experiments based on the data of certain countries have indicated that the QDF method demonstrates the intrinsic characteristics of COVID-19 data of a given country or district, and because the interval of data used in this paper is over one year (500 days), it reveals the fact that after the multi-wave transmission of the coronavirus, the case fatality rate has obviously declined. These results show that the QDF method is effective and feasible as an appraisal method.