Piece-wise linear regression: A new approach to predict COVID-19 spreading

The coronavirus disease 2019 (COVID-19) pandemic is the most rapidly evolving global emergency since March 2020 and one of the most exercised topics in all aspects of the world. So far there are numerous articles that have been published related to COVID-19 in various disciplines of science and soci...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-01, Vol.1020 (1), p.12017
Main Authors: Senapati, Apurbalal, Maji, Soumen, Mondal, Arunendu
Format: Article
Language:English
Subjects:
Coronaviruses
COVID-19
Disease control
Disease transmission
Machine learning
Mathematical analysis
Mathematical model
Piece-wise linear regression
Prediction
Regression analysis
Regression models
Viral diseases
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The coronavirus disease 2019 (COVID-19) pandemic is the most rapidly evolving global emergency since March 2020 and one of the most exercised topics in all aspects of the world. So far there are numerous articles that have been published related to COVID-19 in various disciplines of science and social context. Since from the very beginning, researchers have been trying to address some fundamental questions like how long it will sustain when it will reach the peak point of spreading, what will be the population of infections, cure, or death in the future. To address such issues researchers have been used several mathematical models from the very beginning around the world. The goal of such predictions is to take strategic control of the disease. In most of the cases, the predictions have deviated from the real data. In this paper, a mathematical model has been used which is not explored earlier in the COVID-19 predictions. The contribution of the work is to present a variant of the linear regression model is the piece-wise linear regression, which performs relatively better compared to the other existing models. In our study, the COVID-19 data set of several states of India has been used.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1020/1/012017