Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

  • Leila MOFTAKHAR Student Research Committee, Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Iran
  • Mozhgan SEIF Department of Epidemiology, Faculty of Biostatistics, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
  • Marziyeh Sadat SAFE Mail Seyed-al-Shohada Hospital, Jahrom University of Medical Sciences, Jahrom, Iran
COVID-19;, Forecast, Artificial neural network, Iran


Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran.

Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) models were applied for prediction. The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins. To compare models, dataset was separated into train and test sets. Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria.

Results: Both algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA. While Model comparison confirmed that ARIMA prediction was more accurate than ANN.

Conclusion: COVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.



1. Tang K, Huang Y, Chen M (2020). Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamic model. medRxiv, doi: 10.1101/2020.02.20.20023572.
2. McCall B (2020). COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health, 2(4): 166-7. doi.org/10.1016/S2589-7500(20)30054-6.
3. Song PX, Wang L, Zhou Y et al (2020). An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. medRxiv, doi.org/10.1101/2020.02.29.20029421.
4. Nishiura H, Linton NM, Akhmetzhanov AR (2020). Serial interval of novel coronavirus (COVID-19) infections.IntJInfectDis, 93:284-6. doi: https://doi.org/10.1016/j.ijid.2020.02.060.
5. Hu Z, Ge Q, Jin L, Xiong M (2020). Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:2002.07112.
6. Zhao S, Gao D, Zhuang Z et al (2020). Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020. medRxiv, doi:https://doi.org/10.1101/2020.02.21.20026559.
7. Zhang KK, Xie L, Lawless L, Zhou H, Gao G, Xue C (2020). Characterizing the transmission and identifying the control strategy for COVID-19 through epidemiological modeling. medRxi, doi: https://doi.org/10.1101/2020.02.24.20026773.
8. Wan H, Cui J-a, Yang G-J (2020). Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province. medRxiv, doi: https://doi.org/10.1101/2020.03.01.20029629.
9. Al-qaness MA, Ewees AA, Fan H, Abd El Aziz M (2020). Optimization Method for Forecasting Confirmed Cases of COVID-19 in China. J Clin Med, 9(3): 674.
10. Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Meyers LA (2020). The serial interval of COVID-19 from publicly reported confirmed cases. medRxiv, doi: https://doi.org/10.1101/
11. Anastassopoulou C, Russo L, Tsakris A, Siettos C (2020). Data-Based Analysis, Modelling and Forecasting of the novel Coronavirus (2019-nCoV) outbreak. medRxiv, doi:https://doi.org/ 10.1101/2020.02.11.20022186
12. Ahmadi A, Shirani M, Rahmani F (2020). Modeling and Forecasting Trend of COVID-19 Epidemic in Iran. medRxiv, doi: https://doi.org/10.1101/2020.03.17.20037671.
13. Sun K, Chen J, Viboud C (2020). Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. Lancet Digit Health, e201-e208.
14. Organization WH. Novel coronavirus(2019-nCoV); 2020; Available from URL: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
15. Muniz-Rodriguez K, Fung IC-H, Ferdosi SR et al (2020). Transmission potential of COVID-19 in Iran. medRxiv, doi: https://doi.org/10.1101/
16. https://www.worldometers.info/coronavirus/country/iran/
17. Hue H, Pradit S, Lim A, Goncalo C, Nitiratsuwan T (2018). Shrimp and fish catch landing trends in Songkhla lagoon, Thailand during 2003-2016. Appl Ecol Environ Res, 16(3): 3061-78.
18. Inoue M, Hasegawa S, Suyama A (2011). P1-177 Development and evaluation of a forecasting model for infectious diseases in Japan using time-series analysis. J Epidemiol Community Health, 65(1): A115-A115.
19. Bishop CM (1995). Neural networks for pattern recognition. Oxford university Press.
20. Zhang GP (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-75.
21. Mozhgan S, Faradmal J, Poorolajal J, Mahjub H (2017). Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric SurvivalModels. Iran J Public Health, 46(1): 35-43.
22. Fattah J, Ezzine L, Aman Z, El Moussami H, Lachhab A (2018). Forecasting of demand using ARIMA model. IJEBM, doi: https://doi.org/10.1101/2020.03.13.20035345.
23. Medenwald D, Kuss O (2014). Mortality on match days of the German national soccer team: a time series analysis from 1995 to 2009. J Epidemiol Community Health, 68:869-73.
24. Ansley C, Spivey W, Wrobleski W (1977). A class of transformations for BoxJenkins seasonalmodels. J R Stat Soc Ser C Appl Stat, 26:173-8.
25. Günther F, Fritsch S (2010). neuralnet: Training of neural networks. R j, 2(1): 30-8.
26. Bellazzi R, Zupan B (2008). Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform, 77:81-97.
27. Lee T-T, Liu C-Y, Kuo Y-H, Mills ME, Fong J-G, Hung C (2011). Application of data mining to the identification of critical factors in patient falls using a web-based reporting system. Int J Med Inform, 80(2): 141-50.
28. Teshnizi SH, Ayatollahi SMT (2015). A comparison of logistic regression model and artificial neural networks in predicting of student’s academic failure. Acta Inform Med, 23(5): 296-300.
29. Nakade M, Ojima T, Hirai H, Aida J, Hanibuchi T, Kondo K (2011). P1-259 Relations between BMI and total and cause specific mortality in Japan: ages cohort. J Epidemiol Community Health, 65:A138-A138.
30. Zhan C, Chi KT, Lai Z, Hao T, Su J (2020). Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding. medRxiv, doi: https://doi.org/10.1101/2020.03.08.20032847.
31. Dehesh T, Mardani-Fard H, Dehesh P (2020). Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. medRxiv, doi: https://doi.org/10.1101/2020.03.13.20035345.
32. Zheng Z, Wu K, Yao Z, Zheng J, Chen J (2020). The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model. MedRxiv, doi: https://doi.org/10.1101/2020.03.10.20033670.
How to Cite
MOFTAKHAR L, SEIF M, SAFE MS. Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models. Iran J Public Health. 49(Supple 1):92-100.
Original Article(s)