Forecasting Covid-19 Cases in Türkiye with the Help of LSTM

Abstract

Even though, it is thought that the pandemic has come to an end, the humanity is still under the danger of upcoming pandemics. In that sense, every effort to understand or predict the nature of an infectious disease is very precious since those efforts will provide experience for upcoming infectious disease epidemic/pandemic. Mathematical models provide a common way to analyze the nature of the pandemic. Apart from those mathematical models that mostly determine which variables should be used in the model to predict the nature of the epidemic and at which rate the disease will spread, deep learning models can also provide a fast and practical tool. Moreover, they can shed a light on which variables should be taken into account in the construction of a mathematical model. And also, deep learning methods give rapid results in the robust forecasting trends of the number of new patients that a country will deal with. In this work, a deep learning model that forecasts time series data using a long short-term memory (LSTM) network is used. The time series data used in this project is COVID-19 data taken from the Health Ministry of Republic of Türkiye. The weekend isolation and vaccination are not considered in the deep learning model. It is seen that even though the graph is consistent and similar to the graph of real number of patients, and LSTM is an effective tool to forecast new cases, those parameters, isolation and vaccination, must be taken into account in the construction of mathematical models and also in deep learning models as well.

Keywords

• Long-Short Term Memory (LSTM)
• COVID-19
• Forecasting
• Modeling

References

1. Arino J, Protet S. 2020. A simple model for COVID-19. Infectious Disease Modelling, 5: 309-315.
2. Belen S, Kropat, E, Weber, GW. 2011. On the classical Maki–Thompson rumour model in continuous time. Cent Eur J Oper Res, 19: 1–17.
3. Brauer F, Castillo-Chavez C, Feng Z. 2019. Mathematical models in epidemiology. Springer-Verlag, New York, USA, First Edition, pp: 254.
4. Çifdalöz, O. 2022. Sustainable Management of a Renewable Fishery Resource with Depensation Dynamics from a Control Systems Perspective. Gazi University J Sci, 35 (3): 936-955.
5. Demirci E. 2023. A Novel Mathematical Model of the Dynamics of COVID-19. GU J Sci, 36(3): 1302-1309.
6. Gokgoz N, Oktem H. 2021. Modeling of tumor-immune system interaction with stochastic hybrid systems with memory: a piecewise linear approach. Advances in the Theory of Nonlinear Analysis and its Application, 5(1): 25-38.
7. Graves A, Schmidhuber J. 2008. Offline handwriting recognition with multidimensional recurrent neural networks. Advances in neural information processing systems, 21, 545-552.
8. Jin W, Stokes JM, Eastman RT, Itkin Z, Zakharov AV, Collins JJ, Jaakkola TS, Barzilay R. 2021. Deep learning identifies synergistic drug combinations for treating COVID-19. In: Proceedings of the National Academy of Sciences of the United States of America, 118(39): e2105070118.

9. Livieris IE, Pintelas E, Pintelas, P A. 2020. CNN–LSTM model for gold price time-series forecasting. Neural Comput Applic, 32: 17351–17360.
10. Magar R, Yadav P, Farimani AB. 2021. Potential neutralizing antibodies discovered for novel corona virus using machine learning. Sci Rep, 11: 5261.
11. Paul SG, Saha A, Biswas A, Zulfiker S, Arefin MS, Rahman M, Reza AW. 2023. Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives. Array, 2023: 100271.
12. Pedamallu CS, Özdamar L, Kropat E, Weber GW. 2012. A system dynamics model for intentional transmission of HIV/AIDS using cross impact analysis. CEJOR, 20(2): 319-336.
13. Sagheer A, Kotb M. 2019. Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomput, 323: 203-213.
14. Siami-Namini S, Tavakoli N, Siami Namin A. 2018. A Comparison of ARIMA and LSTM in Forecasting Time Series. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA): pp: 1394-1401. doi: 10.1109/ICMLA.2018.
15. Subramanian N, Elharrouss O, Al-Maadeed S, Chowdhury M. 2022. A review of deep learning-based detection methods for COVID-19. Computers Biol Med, 2022: 105233
16. Tealab A. 2018. Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing Informatics J, 3(2): 334-340.
17. Toğaçar M, Ergen B, Cömert Z. 2020. COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med, 121: 103805.
18. Vega DI. 2020. Lockdown, one, two, none, or smart. Modeling containing COVID-19 infection. A conceptual model. Sci Total Environ, 730: 138917.
19. Wang P, Zheng X, Ai G, Liu D, Zhu B. 2020. Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: case studies in Russia, Peru and Iran. Chaos Solit Fractals, 140.
20. Xu L, Magar R, Farimani AB. 2022. Forecasting COVID-19 new cases using deep learning methods. Computers Biol Med, 2022: 105342.
21. Yunpeng L, Di H, Junpeng B, Yong Q. 2017. Multi-step ahead time series forecasting for different data patterns based on lstm recurrent neural network. In: 14th Web Information Systems and Applications Conference (WISA): pp: 305-310. doi: 10.1109/WISA.2017.25.