Trend and prediction of COVID-19 outbreak in Iran: SEIR and ANFIS model

• Autorzy: Shafiekhani Sajad , Khalilabad Touraj Harati , Rafiei Sima , Sadeghi Vahid , Jafari Amir Homayoun , Gheibi Nematollah

Identyfikatory

DOI

10.2478/pjmpe-2021-0029

• Języki publikacji: EN

Abstrakty

EN

Background: Mathematical and predictive modeling approaches can be used in COVID-19 crisis to forecast the trend of new cases for healthcare management purposes. Given the COVID-19 disease pandemic, the prediction of the epidemic trend of this disease is so important. Methods: We constructed an SEIR (Susceptible-Exposed-Infected-Recovered) model on the COVID-19 outbreak in Iran. We estimated model parameters by the data on notified cases in Iran in the time window 1/22/2020 – 20/7/2021. Global sensitivity analysis is performed to determine the correlation between epidemiological variables and SEIR model parameters and to assess SEIR model robustness against perturbation to parameters. We Combined Adaptive Neuro- Fuzzy Inference System (ANFIS) as a rigorous time series prediction approach with the SEIR model to predict the trend of COVID-19 new cases under two different scenarios including social distance and non-social distance. Results: The SEIR and ANFIS model predicted new cases of COVID-19 for the period February 7, 2021, till August 7, 2021. Model predictions in the non-social distancing scenario indicate that the corona epidemic in Iran may recur as an immortal oscillation and Iran may undergo a recurrence of the third peak. Conclusion: Combining parametrized SEIR model and ANFIS is effective in predicting the trend of COVID-19 new cases in Iran.

Słowa kluczowe

EN

coronavirus disease 2019; COVID-19; Susceptible-Exposed-Infectious-Recovered; SEIR; Adaptive Neuro-Fuzzy Inference System; ANFIS; global sensitivity analysis

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2021
• Tom: Vol. 27, Iss. 3
• Strony: 241--249
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Shafiekhani Sajad

• Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
• Research Center for Biomedical Technologies and Robotics, Tehran, Iran
• Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran

autor

Khalilabad Touraj Harati

• Department of Health Economics, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

autor

Rafiei Sima

• Department of Healthcare Management, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran

autor

Sadeghi Vahid

• Departments of Biomedical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

autor

Jafari Amir Homayoun

• Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
• Research Center for Biomedical Technologies and Robotics, Tehran, Iran

autor

Gheibi Nematollah

• Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran

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