ArticleResults in physics2021
On the accuracy of ARIMA based prediction of COVID-19 spread.
Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
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- Information-Entropic Deep Learning with Gaussian Process Regularisation for Uncertainty-Aware Quantitative Trading.Entropy (Basel, Switzerland) · 2026Article
- Childhood sepsis burden: pathogens/antimicrobial-resistant bacteria, 1990-2021 and 2050 forecasts.Frontiers in cellular and infection microbiology · 2026Article
- Time series modelling and forecasting of mpox incidence and mortality in Nigeria.BMC infectious diseases · 2025Article
- Navigating Samarinda's climate: A comparative analysis of rainfall forecasting models.MethodsX · 2025Article
- Impact of COVID-19 vaccinations in India: a state-wise analysis.BMC public health · 2025Article
- Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection.Frontiers in medicine · 2025Article
- Analysis and forecast of hand, foot, and mouth disease epidemic trends in Guangzhou, China, 2013-2023.PloS one · 2025Article
- Global burden of chronic kidney disease due to diabetes mellitus, 1990-2021, and projections to 2050.Frontiers in endocrinology · 2025Article
- Health system resilience during the COVID-19 pandemic: A comparative analysis of disruptions in care from 32 countries.Health services research · 2024Article
- A prediction model of PTSD in the Israeli population in the aftermath of october 7th, 2023, terrorist attack and the Israel-Hamas war.Israel journal of health policy research · 2024Article
- Long-Term Passenger Flow Forecasting for Rail Transit Based on Complex Networks and Informer.Sensors (Basel, Switzerland) · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- The Future of Total Elbow Arthroplasty: A Statistical Forecast Model for Germany.Healthcare (Basel, Switzerland) · 2024Article
- An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere.Fundamental research · 2024Review
- Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.BMC infectious diseases · 2024Article
- Estimating All-Cause Deaths Averted in the First Two Years of the COVID-19 Vaccination Campaign in Italy.Vaccines · 2024Article
- Epidemiology of respiratory syncytial virus in a large pediatric hospital in Central Italy and development of a forecasting model to predict the seasonal peak.Italian journal of pediatrics · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible-Infected-Recovered model, Susceptible-Exposed-Infected-Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait's gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson's correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.
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