Forecasting COVID-19 Cases using Multiple Statistical Models

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 sig...

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Bibliographic Details
Published in2020 8th International Conference on Orange Technology (ICOT) pp. 1 - 5
Main Authors Saeed, Faisal, Paul, Anand, Ahmed, Muhammad Jamal
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.12.2020
Subjects
ARIMA
Arithimetic Equation
COVID-19
Epidemic
Epidemics
Linear regression
Mathematical model
Predictive models
Regulation
Social implications of technology
Vector Auto Regression
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DOI10.1109/ICOT51877.2020.9468784

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Summary: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.
DOI:10.1109/ICOT51877.2020.9468784