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

TL;DR

This paper introduces a novel quasi-distribution fitting method using piecewise B-spline curves to analyze COVID-19 data, revealing pandemic characteristics and trends over a year-long period.

Contribution

It develops a new fitting approach based on B-spline curves that models COVID-19 data as probability density functions for pandemic analysis.

Findings

The method captures intrinsic COVID-19 data characteristics.

It shows a decline in case fatality rate after multiple waves.

The approach is effective over a one-year data span.

Abstract

Facing the world wide coronavirus disease 2019 (COVID-19) pandemic, a new fitting method (QDF, quasi-distribution fitting) which could 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 the COVID-19 with piecewise quasi-uniform B-spline curves. Being dealt with 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 demonstrate the intrinsic characteristics of COVID-19 data of the given country or distric, and because of the interval of data used in this paper is over one year (500 days), it reveals the fact that after multi-wave transmission of the coronavirus, the case fatality rate has declined obviously, the result shows that as an appraisal method, it is effective and feasible.