ArticleBiomedical signal processing and control2021
Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
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- Modeling the seasonal epidemic of human brucellosis in China: A comparative time series analysis.PloS one · 2026Article
- Cyber-resilient machine learning framework for accurate individual load forecasting and anomaly detection in smart grids.Scientific reports · 2025Article
- Stroke Mortality in Kazakhstan: Comparison of National Health Records to Global Burden of Disease Study.JACC. Asia · 2025Article
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- Spatial Distribution Analysis and Comparative Forecasting of Dengue Resurgence in the Philippines (2025-2027): A Nationwide Study.Transboundary and emerging diseases · 2025Article
- Trends and multi-model prediction of hepatitis B incidence in Xiamen.Infectious Disease Modelling · 2024Article
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
- Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach.Biomedical signal processing and control · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data.Frontiers in veterinary science · 2023Article
- Forecasting adversities of COVID-19 waves in India using intelligent computing.Innovations in systems and software engineering · 2022Article
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy.The European journal of health economics : HEPAC : health economics in prevention and care · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level.Scientific reports · 2022Article
- A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.International journal of environmental research and public health · 2022Article
- Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019.ISA transactions · 2022Article
- Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach.Computers in biology and medicine · 2022Article
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4 authors.
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Abstract
backgroundThe COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence.
methodsIn this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020.
resultsOur findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2484 new confirmed and 114 new death cases of COVID-19.
conclusionAccording to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.
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