A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

Abolmaali, Saina; Shirzaei, Samira

doi:10.3934/publichealth.2021048

Cited by 27 publications

(22 citation statements)

References 42 publications

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“…Among the latter, ARIMA (autoregressive integrated moving average) and SARIMA (ARIMA + seasonal component) models have shown excellent predictive capabilities. In particular, a recent study by Abolmaali and Shirzaei demonstrated that the ARIMA approach could outperform other classical models such as logistic function, linear regression, and SIR [11]. Similar findings were obtained by Alabdulrazzaq et al, who proved that the accuracy of the prediction of COVID-19 spread provided by their ARIMA model was both appropriate and satisfactory [12].…”

Section: Introductionsupporting

confidence: 69%

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

Rovetta¹,

Bhagavathula

2021

Preprint

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Background: COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. Objective: This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. Methods: We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in the year 2020 as compared to death reported in 2020. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predictors and observed death during the year 2020. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during the year 2020 is between 640,000 and 660,000 (95% confidence intervals range: 620,000 - 695,000) and these values were far from the observed deaths reported (n = 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001) Conclusions: Our findings suggest that the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy theorists. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

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Section: Introductionsupporting

confidence: 69%

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

Rovetta¹,

Bhagavathula

2021

Preprint

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“…Abolmaali & Shirzaei [11] have compared the results of different models (SIR Model, linear regression, logistic function, ARIMA) in the prediction of confirmed COVID-19 cases in 2021. Although the linear regression model performs well in short-term prediction, overall, the ARIMA model is still better than other models.…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US

Shih

et al. 2022

Mathematics

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In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)– appeared for the first time, breaking out in Wuhan, China, and the epidemic spread quickly to the world in a very short period time. According to WHO data, ten million people have been infected, and more than one million people have died; moreover, the economy has also been severely hit. In an outbreak of an epidemic, people are concerned about the final number of infections. Therefore, effectively predicting the number of confirmed cases in the future can provide a reference for decision-makers to make decisions and avoid the spread of deadly epidemics. In recent years, the α-Sutte indicator method is an excellent predictor in short-term forecasting; however, the α-Sutte indicator uses fixed static weights. In this study, by adding an error-based dynamic weighting method, a novel β-Sutte indicator is proposed. Combined with ARIMA as an ensemble model (βSA), the forecasting of the future COVID-19 daily cumulative number of cases and the number of new cases in the US are evaluated from the experiment. The experimental results show that the forecasting accuracy of βSA proposed in this study is better than other methods in forecasting with metrics MAPE and RMSE. It proves the feasibility of adding error-based dynamic weights in the β-Sutte indicator in the area of forecasting.

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“…The findings demonstrate that Pearson VII universal kernel-based Support Vector Machine outperform other methods by achieving 98.81% accuracy and MAE of 0.012. To predict the number of cases in four nations, Abolmaali and Shirzaei ( 2021 ) employed logistic function, linear regression, Susceptible-Infected-Recovered (SIR) model, and ARIMA model. The models are evaluated using MSE.…”

Section: Related Workmentioning

confidence: 99%

Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up

Vig

Kaur²

2022

Int J Syst Assur Eng Manag

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COVID-19 has spread around the world since it begun in December 2019. The pandemic has created an unprecedented global health emergency since World War II. This paper studies the impact of pandemic and predicts the anticipated casualty rise in India. The data has been extracted from the API provided by https://www.covid19india.org/ and covers up the time period from 30th January 2020 when the first case occurred in India till 13th January 2021. The paper provides a comparative study of six machine learning algorithms namely SMOreg, Random Forest, lBk, Gaussian Process, Linear Regression, and Autoregressive Integrated Moving Average (ARIMA) in forecasting deceased COVID 19 cases, via the data mining tool such as Weka and R. The major findings show that the best predictor model for anticipating the frequency of deceased cases in India is ARIMA (5,2,0). Utilizing this model, we estimated the propagation rate of deceased cases for the next month. The findings reveal that the fatal cases in India could rise from 151,174 to 157,179 within one month with an average of 190 death reports every day. This study will be helpful for the Indian Government and Medical Practitioners in assessing the spread of pandemic in India and devising a combat plan to mitigate the pandemic.

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A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

Cited by 27 publications

References 42 publications

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US

Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up

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