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

doi:10.1109/CAMAD50429.2020.9209264

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728163390
DOIs	https://doi.org/10.1109/CAMAD50429.2020.9209264
Publication status	Published - Sept 2020
Event	25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Pisa, Italy Duration: 14 Sept 2020 → 16 Sept 2020

Publication series

Name	IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
Volume	2020-September
ISSN (Electronic)	2378-4873

Conference

Conference	25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020
Country/Territory	Italy
City	Pisa
Period	14/09/20 → 16/09/20

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CAMAD50429.2020.9209264

Cite this

Andreas, A., Mavromoustakis, C. X., Mastorakis, G., Mumtaz, S., Batalla, J. M., & Pallis, E. (2020). Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections. In 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings Article 9209264 (IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD; Vol. 2020-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMAD50429.2020.9209264

Andreas, Andreou ; Mavromoustakis, Constandinos X. ; Mastorakis, George et al. / Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD).

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title = "Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections",

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.",

keywords = "cloud computing, coronavirus, covid19, curve fitting, epidemic, forecast, machine learning, pandemic, regression",

author = "Andreou Andreas and Mavromoustakis, {Constandinos X.} and George Mastorakis and Shahid Mumtaz and Batalla, {Jordi Mongay} and Evangelos Pallis",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 ; Conference date: 14-09-2020 Through 16-09-2020",

year = "2020",

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doi = "10.1109/CAMAD50429.2020.9209264",

language = "English",

series = "IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD",

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Andreas, A, Mavromoustakis, CX, Mastorakis, G, Mumtaz, S, Batalla, JM & Pallis, E 2020, Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections. in 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings., 9209264, IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD, vol. 2020-September, Institute of Electrical and Electronics Engineers Inc., 25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020, Pisa, Italy, 14/09/20. https://doi.org/10.1109/CAMAD50429.2020.9209264

Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections. / Andreas, Andreou; Mavromoustakis, Constandinos X.; Mastorakis, George et al.
2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9209264 (IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

AU - Andreas, Andreou

AU - Mavromoustakis, Constandinos X.

AU - Mastorakis, George

AU - Mumtaz, Shahid

AU - Batalla, Jordi Mongay

AU - Pallis, Evangelos

PY - 2020/9

Y1 - 2020/9

N2 - 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.

AB - 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.

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KW - curve fitting

KW - epidemic

KW - forecast

KW - machine learning

KW - pandemic

KW - regression

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M3 - Conference contribution

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Andreas A, Mavromoustakis CX, Mastorakis G, Mumtaz S, Batalla JM, Pallis E. Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections. In 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9209264. (IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD). doi: 10.1109/CAMAD50429.2020.9209264

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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