Predict The Spread of COVID-19 in Iran with A SEIR Model

Shirin Kordnoori, Mahboobe Sadat Kobari, Hamidreza Mostafaei

Abstract


The current coronavirus disease 2019 (COVID-19) outbreak has recently been declared a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. This paper designed a predictive model based on the mathematical model Susceptible-Exposed-Infective-Recovered (SEIR). SEIR is represented by a set of differential-algebraic equations incorporated with machine learning techniques to fit the data reported to estimate the spread of the COVID-19 epidemic in long-term in the Islamic Republic of Iran up to the end of July 0f 2020. This paper reduced R0 after a certain amount of days to account for containment measures and used delays to allow for lagging official data. Two evaluation criteria, R2 and RMSE, had used in this research which estimates the model on officially reported confirmed cases from different regions in Iran. The results proved the model’s effectiveness in simulating and predicting the trend of the COVID-19 outbreak. Results showed the integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak.

Keywords


COVID-19; Epidemic Peak; Generalized Additive Models; SEIR Model

Full Text:

Full Text

References


Zhong L, Mu L, Li J, Wang J, Yin Z, Liu D. Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model. IEEE Access 2020;8:51761–51769.

Nesteruk IG. Statistics Based Models for the Dynamics of Chernivtsi Children Disease. Research Bulletin of the National Technical University of Ukraine "Kyiv Politechnic Institute" 2017;5:26–34.

Neher RA, Dyrdak R, Druelle V, Hodcroft EB, Albert J. Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss Medical Weekly 2020;150(w20224):1–8.

Caccavo D. Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. medRxiv 2020;https://www.medrxiv.org/content/early/2020/04/21/2020.03.19.20039388.

Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19outbreak. PLoS ONE 2020;15(3):1–21.

Khan MA, Atangana A. Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative. Alexandria Engineering Journal 2020;59(4):2379–2389.

Peng L, Yang W, Zhang D, Zhuge C, Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. medRxiv 2020;https://www.medrxiv.org/content/early/2020/02/18/2020.02.16.20023465.

Bacaër N. A short history of mathematical population dynamics. 1 ed. London: Springer; 2011. https://link.springer.com/book/10.1007%2F978-0-85729-115-8.

Ellerin T, Farid H, Krakower D, LeWine HE, McCarthy C, Memon B, et al., Coronavirus Resource Center - Harvard Health; 2021. https://www.health.harvard.edu/diseases-and-conditions/coronavirus-resource-center.

Xu B, Gutiérrez B, Mekaru S, Sewalk K, Goodwin L, Loskill A, et al. Epidemiological data from the COVID-19 outbreak, real-time case information. Scientific Data 2020;7.

Putra S, Muktamar K, Zulkarnain. Estimation of Parameters in the SIR Epidemic Model Using Particle Swarm Optimization. American Journal of Mathematical and Computer Modelling 2019;4(4):83–93.

Mbuvha R, Marwala T. On Data-Driven Management of the COVID-19 Outbreak in South Africa. medRxiv 2020;https://www.medrxiv.org/content/early/2020/04/15/2020.04.07.20057133.

Qi H, Xiao S, Shi R, Ward MP, Chen Y, Tu W, et al. COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Science of The Total Environment 2020;728:138778. https://www.sciencedirect.com/science/article/pii/S0048969720322956.

Salgotra R, Gandomi M, Gandomi AH. Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming. Chaos, Solitons & Fractals 2020;138:109945. https://www.sciencedirect.com/science/article/pii/S0960077920303441.

Zareie B, Roshani A, Mansournia MA, Rasouli MA, Moradi G. A Model for COVID-19 Prediction in Iran Based on China Parameters. Arch Iran Med 2020;23(4):244–248. http://aimjournal.ir/Article/aim-15640.

Lijuan Z, Fuchang W, Xuqin Z, Zhitong J. Global Stability Analysis on One Type of SEIR Epidemic Model with Floating Population. Journal of Institute of Disaster-Prevention Science and Technology 2019;21(2):78–81.

Safi MA, Garba SM. Global stability analysis of SEIR model with holling type II incidence function. Computational and Mathematical Methods in Medicine 2012;2012:1–8.

Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease 2020;12(3). https://jtd.amegroups.com/article/view/36385.

Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, et al. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. International Journal of Infectious Diseases 2020 apr;93:211–216.




DOI: http://dx.doi.org/10.12962/j20882033.v32i1.7227

Refbacks

  • There are currently no refbacks.


Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.