COVID-19 Cases in Iraq; Forecasting Incidents Using Box - Jenkins ARIMA Model

Mustafa, Hadeel Ismail; Fareed, Noor Yousif

doi:10.1109/nicst50904.2020.9280304

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Many scholars have researched COVID-19 virus infection predictions. All previous research has established that the ARIMA model is the most effective for forecasting (Benvenuto et al, 2020;Jain et al, 2021;Murugesan et al, 2020;Mustafa & Fareed, 2020;Sahai et al, 2020;Sulasikin et al, 2020). Sulasikin et al (2020) used three approaches to predict the COVID-19 instances (Holt's method, Holt-Winters method, and ARIMA).…”

Section: Time-series Analysis To Predict Covid-19 Casesmentioning

confidence: 99%

Exploring the impact of air pollution on COVID-19 admitted cases

Alsaber

Setiya

Al-Sultan

et al. 2022

Jpn J Stat Data Sci

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In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants ($$\hbox {O}_3$$ O 3 , $$\hbox {SO}_2$$ SO 2 , $$\hbox {NO}_2$$ NO 2 , $$\hbox {CO}$$ CO , and $$\hbox {PM}_{10}$$ PM 10 ) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants ($$\hbox {O}_3$$ O 3 , $$\hbox {PM}_{10}$$ PM 10 , $$\hbox {NO}_2$$ NO 2 , temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide ($$\hbox {SO}_2$$ SO 2 ), $$\hbox {NO}_2$$ NO 2 , temperature, and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants ($$\hbox {O}_3$$ O 3 , $$\hbox {SO}_2$$ SO 2 , $$\hbox {NO}_2$$ NO 2 , $$\hbox {CO}$$ CO , and $$\hbox {PM}_{10}$$ PM 10 ) has a significant impact on COVID-19-admitted cases’ prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.

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Section: Time-series Analysis To Predict Covid-19 Casesmentioning

confidence: 99%

Exploring the impact of air pollution on COVID-19 admitted cases

Alsaber

Setiya

Al-Sultan

et al. 2022

Jpn J Stat Data Sci

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“…Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used as the evaluation parameters. Hadeel I. Mustafa et al [8] did forecasting based on a dataset published by the Iran health ministry regarding the COVID19 breakout. ARIMA (2,1,5) model gives the best prediction results with the performance evaluation being done by MSE and MAE.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID19 Prediction using Time Series Analysis

Jain

Sukhdeve

Gadia

et al. 2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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The ongoing COVID19 pandemic has created havoc all over the world. Millions of lives have been gone and thousands are vulnerable. It has also affected the world economy due to lockdown. So, there is a need to develop a time-series forecasting model for predicting future cases so that necessary precautions can be taken. The aim is to help in coping up with the situation without affecting lifestyle any further. For forecasting, this paper has considered four models: exponential smoothing, ARIMA and SARIMA models, and identifies the suitability of the model for prediction purposes. Also, to incorporate the impact of festive seasons in India to measure the fluctuations in the new cases, SARIMAX model is used. The spread case data of the pandemic collected is of 47 weeks, from 30th January 2020 to 23rd December 2020 for India. Data of the first 45 weeks (90%) is taken for training the model and that of the last 2 weeks (10%) is used for validation purposes. For evaluation purposes RMSE (root mean square error) and MAE (mean absolute error) are taken as parameters for model evaluation. The ARIMA (8,2,1) model performed best among all models based on the RMSE. Considering multiplicative trend and seasonality Triple Exponential Smoothing model gave the best result with respect to MAE.

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