Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

Andreou Andreas, Constandinos X. Mavromoustakis, George Mastorakis, Shahid Mumtaz, Jordi Mongay Batalla, Evangelos Pallis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

COrona VIrus Disease 2019 (COVID-19) is a disease caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and was first diagnosed in China in December, 2019. Dr. Tedros Adhanom Ghebreyesus, World Health Organization (WHO) director-general on March 11th declared the COVID-19 pandemic. The cumulative cases of infected individuals and deaths due to COVID-19 develop a graph that could be interpreted by an exponential function. Mathematical models are therefore fundamental to understanding the evolution of the pandemic. Applying machine learning prediction methods in conjunction with cloud computing to such models will be beneficial in designing effective control strategies for the current or future spread of infectious diseases. Initially, we compare the trendlines of the following three models: linear, exponential and polynomial using R-squared, to determine which model best interprets the prevailing data sets of cumulative infectious cases and cumulative deaths due to COVID-19 disease. We propose the development of an improved mathematical forecasting framework based on machine learning and the cloud computing system with data from a real-time cloud data repository. Our goal is to predict the progress of the curve as accurately as possible in order to understand the spread of the virus from an early stage so that strategies and policies can be implemented.

Original languageEnglish
Title of host publication2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163390
DOIs
Publication statusPublished - Sep 2020
Event25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Pisa, Italy
Duration: 14 Sep 202016 Sep 2020

Publication series

NameIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
Volume2020-September
ISSN (Electronic)2378-4873

Conference

Conference25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020
Country/TerritoryItaly
CityPisa
Period14/09/2016/09/20

Keywords

  • cloud computing
  • coronavirus
  • covid19
  • curve fitting
  • epidemic
  • forecast
  • machine learning
  • pandemic
  • regression

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