COVID-19 Data Fitting with Linear and Nonlinear RegressionMatlab Package, April 15, 2020 / Lorand Gabriel Parajdi

Published at MATLAB Central File Exchange https://www.mathworks.com/matlabcentral/fileexchange/75016-covid19-data-fitting-with-linear-and-nonlinear-regression.

Authors: Lorand Gabriel Parajdi¹ and Ioan Stefan Haplea²

¹ Department of Mathematics, “Babeş–Bolyai” University, Cluj-Napoca, Romania² Department of Internal Medicine, “Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania

Abstract: A collection of tools for fitting several general-purpose linear and nonlinear models for COVID-19 epidemiological data. The longitudinal data is obtained from the John Hopkins database (source: https://github.com/CSSEGISandData/COVID-19) and consists of: number of active cases, number of confirmed, number of fatalities, number of recovered cases. The analysis is possible for any particular country listed in the database, or for the world data as a whole. The models implemented include linear, exponential, logistic, Gompertz, fifth-degree polynomial, Gaussians and Fourier functions. The three models of the Bertalanffy class (exponential, proper logistic and Gompertz) afford a reasonable balance between reduced model complexity and goodness of fit. We implement data/model visualization in linear and logarithmic scales, for easy model comparisons.

Keywords: Coronavirus; Covid19; Data fitting; Epidemiology; Exponential; Fourier; Gaussian; Gompertz; Linear; Logistic; Linear; Pandemic; Regression

Cite As: Lorand Gabriel Parajdi and Ioan Stefan Haplea (2020). COVID-19 Data Fitting with Linear and Nonlinear Regression (https://www.mathworks.com/matlabcentral/fileexchange/75016-covid19-data-fitting-with-linear-and-nonlinear-regression), MATLAB Central File Exchange. Retrieved April 15, 2020.