Forecasting COVID-19 cases using multiple statistical models

Faisal Saeed; Anand Paul; Muhammad Jamal Ahmed

doi:10.1109/ICOT51877.2020.9468784

Forecasting COVID-19 cases using multiple statistical models

Faisal Saeed
, Anand Paul
, Muhammad Jamal Ahmed

Kyungpook National University

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

3 Scopus citations

Abstract

An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with Linear regression and the Autoregressive Integrated Moving Average model (ARIMA). The used methods for calculating the growth rates are different in every four cases. For ARIMA, the constraints are predicted by ACF and PACF correlogram. Simulated results show a better performance for each model.

Original language	English
Title of host publication	2020 8th International Conference on Orange Technology, ICOT 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665418522
DOIs	https://doi.org/10.1109/ICOT51877.2020.9468784
State	Published - 18 Dec 2020
Event	8th International Conference on Orange Technology, ICOT 2020 - Daegu, Korea, Republic of Duration: 18 Dec 2020 → 21 Dec 2020

Publication series

Name	2020 8th International Conference on Orange Technology, ICOT 2020

Conference

Conference	8th International Conference on Orange Technology, ICOT 2020
Country/Territory	Korea, Republic of
City	Daegu
Period	18/12/20 → 21/12/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

ARIMA
Arithimetic Equation
COVID-19
Epidemic
Vector Auto Regression

Access to Document

10.1109/ICOT51877.2020.9468784

Cite this

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title = "Forecasting COVID-19 cases using multiple statistical models",

abstract = "An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with Linear regression and the Autoregressive Integrated Moving Average model (ARIMA). The used methods for calculating the growth rates are different in every four cases. For ARIMA, the constraints are predicted by ACF and PACF correlogram. Simulated results show a better performance for each model.",

keywords = "ARIMA, Arithimetic Equation, COVID-19, Epidemic, Vector Auto Regression",

author = "Faisal Saeed and Anand Paul and Ahmed, \{Muhammad Jamal\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th International Conference on Orange Technology, ICOT 2020 ; Conference date: 18-12-2020 Through 21-12-2020",

year = "2020",

month = dec,

day = "18",

doi = "10.1109/ICOT51877.2020.9468784",

language = "English",

series = "2020 8th International Conference on Orange Technology, ICOT 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 8th International Conference on Orange Technology, ICOT 2020",

address = "United States",

}

Saeed, F, Paul, A & Ahmed, MJ 2020, Forecasting COVID-19 cases using multiple statistical models. in 2020 8th International Conference on Orange Technology, ICOT 2020., 9468784, 2020 8th International Conference on Orange Technology, ICOT 2020, Institute of Electrical and Electronics Engineers Inc., 8th International Conference on Orange Technology, ICOT 2020, Daegu, Korea, Republic of, 18/12/20. https://doi.org/10.1109/ICOT51877.2020.9468784

Forecasting COVID-19 cases using multiple statistical models. / Saeed, Faisal; Paul, Anand; Ahmed, Muhammad Jamal.
2020 8th International Conference on Orange Technology, ICOT 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9468784 (2020 8th International Conference on Orange Technology, ICOT 2020).

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

TY - GEN

T1 - Forecasting COVID-19 cases using multiple statistical models

AU - Saeed, Faisal

AU - Paul, Anand

AU - Ahmed, Muhammad Jamal

PY - 2020/12/18

Y1 - 2020/12/18

N2 - An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with Linear regression and the Autoregressive Integrated Moving Average model (ARIMA). The used methods for calculating the growth rates are different in every four cases. For ARIMA, the constraints are predicted by ACF and PACF correlogram. Simulated results show a better performance for each model.

AB - An epidemic of respirational sickness triggered by a different coronavirus. Throughout the duration of an outbreak when the individual-to-individual transmission is traditional and testified subjects of coronavirus sickness 'COVID-19' are intensifying globally. Predicting is of extreme significance for medic-care devising and regulation the disease with the inadequate property. For the sake of prediction, in this paper, we have used the arithmetic equations with different growth rates along with Linear regression and the Autoregressive Integrated Moving Average model (ARIMA). The used methods for calculating the growth rates are different in every four cases. For ARIMA, the constraints are predicted by ACF and PACF correlogram. Simulated results show a better performance for each model.

KW - ARIMA

KW - Arithimetic Equation

KW - COVID-19

KW - Epidemic

KW - Vector Auto Regression

UR - https://www.scopus.com/pages/publications/85112441810

U2 - 10.1109/ICOT51877.2020.9468784

DO - 10.1109/ICOT51877.2020.9468784

M3 - Conference contribution

AN - SCOPUS:85112441810

T3 - 2020 8th International Conference on Orange Technology, ICOT 2020

BT - 2020 8th International Conference on Orange Technology, ICOT 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th International Conference on Orange Technology, ICOT 2020

Y2 - 18 December 2020 through 21 December 2020

ER -

Forecasting COVID-19 cases using multiple statistical models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this