Using generalized logistics regression to forecast population infected by Covid-19

TL;DR

This paper introduces a method using generalized logistic regression curve fitting to forecast Covid-19 infected populations, aiming to improve prediction accuracy in epidemiological modeling.

Contribution

The paper presents a novel application of generalized logistic regression for forecasting Covid-19 infection populations, expanding its use beyond traditional population growth and survival analysis.

Findings

Effective modeling of Covid-19 infection growth curves

Potential for improved epidemic forecasting accuracy

Versatility of the generalized logistic regression approach

Abstract

In this work, a proposal to forecast the populations using generalized logistics regression curve fitting is presented. This type of curve is used to study population growth, in this case population of people infected with the Covid-19 virus; and it can also be used to approximate the survival curve used in actuarial and similar studies.