FORECASTING INFECTION FATALITY RATE OF COVID-19, MEASURING THE EFFICIENCY OF SEVERAL HYBRID MODELS



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Abstract

Abstract

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis.

Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural  Time  Series).

Results: To assess the performance of these models, we employ key  evaluation metrics, including Root Mean Squared Error (RMSE), Mean  Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model  RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other  model ARIMA, BATS, RF and SVM.

Conclusion: This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.  

About the authors

Djillali Seba

Ecole Supèrieure en Informatique Sidi-Belabbes, Algeri;
Department of Mathematics, Applied Mathematics Laboratory, University of Bejaia, Bejaia, Algeria

Email: djillali.seba@univ-bejaia.dz

Ph.D in Mathematics, Speciality probability and Statistics from the university of Bejaia. Assistant at Ecole superieure en informatique Sidi-Belabbes, member of applied mathematics laboratory, University of Bejaia

Algeria

Karima Belaide

Ecole Supèrieure en Informatique Sidi-Belabbes, Algeria;
Department of Mathematics, Applied Mathematics Laboratory, University of Bejaia, Bejaia, Algeria

Author for correspondence.
Email: karima.belaide@univ-bejaia.dz

Ph.D in Mathematics, Speciality probability and Statistics from the University of Houari Boumediene Algiers. Full Professor at the university of Bejaia. Director of applied Mathematics Laboratory. University of Bejaia

Algeria

References

  1. Acosta-Gonzalez, E., Andrada-F´elix, J., Fernandez-Rodriguez, F. (2022). On the evo- lution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms. Mathematics and Computers in Simulation, 197, 91-104.
  2. Ahmar, A. S., Boj, E. (2021). Application of neural network time series (Nnar) and arima to forecast infection fatality rate (ifr) of covid-19 in Brazil. JOIV: International Journal on Informatics Visualization, 5(1), 8-10.
  3. Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., Alhyari, S. (2020). COVID-19 prediction and detection using deep learning. International Journal of Com- puter Information Systems and Industrial Management Applications, 12(June), 168- 181.
  4. Alzahrani, S. I., Aljamaan, I. A., Al-Fakih, E. A. (2020). Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. Journal of infection and public health, 13(7), 914-919.
  5. ArunKumar, K. E., Kalaga, D. V., Kumar, C. M. S., Kawaji, M., Brenza, T. M. (2022). Comparative analysis of Gated Recurrent Units (GRU), long Short-Term mem- ory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal au- toregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria engineering journal, 61(10), 7585-7603.
  6. Beran, J. (1994). Statistics for long-memory processes (Vol. 61). CRC press.
  7. Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley and Sons.
  8. Dehesh, T., Mardani-Fard, H. A., Dehesh, P. (2020). Forecasting of covid-19 confirmed cases in different countries with arima models. MedRxiv, 2020-03.
  9. Chen, J. M. (2022). Novel statistics predict the COVID-19 pandemic could terminate in 2022. Journal of Medical Virology, 94(6), 2845-2848.
  10. Deif, M. A., Solyman, A. A., Hammam, R. E. (2021). ARIMA model estimation based on genetic algorithm for COVID-19 mortality rates. International Journal of Information Technology and Decision Making, 20(06), 1775-1798.
  11. De Livera, A. M., Hyndman, R. J., Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American statistical association, 106(496), 1513-1527.
  12. Kapoor, A., Ben, X., Liu, L., Perozzi, B., Barnes, M., Blais, M., O’Banion, S. (2020). Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113.
  13. Lukman, A. F., Rauf, R. I., Abiodun, O., Oludoun, O., Ayinde, K., Ogundokun,
  14. R. O. (2020). COVID-19 prevalence estimation: Four most affected African countries. Infectious Disease Modelling, 5, 827-838.
  15. Namasudra, S., Dhamodharavadhani, S., Rathipriya, R. (2021). Nonlinear neural network based forecasting model for predicting COVID-19 cases. Neural processing letters, 1-21.
  16. Rath, S., Tripathy, A., Tripathy, A. R. (2020). Prediction of new active cases of coro- navirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes metabolic syndrome: clinical research and reviews, 14(5), 1467-1474.
  17. Roy, S., Bhunia, G. S., Shit, P. K. (2021). Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Modeling earth systems and environment, 7, 1385- 1391.
  18. Salgotra, R., Gandomi, M., Gandomi, A. H. (2020). Time series analysis and forecast of the COVID-19 pandemic in India using genetic programming. Chaos, Solitons and Fractals, 138, 109945.
  19. Singh, A., Bajpai, M. K. (2020). SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement. medRxiv, 2020-07.
  20. Tamang, S. K., Singh, P. D., Datta, B. (2020). Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique. Global Journal of Environmental Science and Management, 6(Special Issue (Covid-19)), 53-64.
  21. Tipping, M. (1999). The relevance vector machine. Advances in neural information processing systems, 12.
  22. Vapnik, V. The Nature of Statistical Learning Theory. Springer, New York, 1995.
  23. Vattay, G. (2020). Forecasting the outcome and estimating the epidemic model pa- rameters from the fatality time series in COVID-19 outbreaks. Physical Biology, 17(6), 065002.
  24. Yousaf, M., Zahir, S., Riaz, M., Hussain, S. M., Shah, K. (2020). Statistical anal- ysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons and Fractals, 138, 109926.
  26. Zeroual, A., Harrou, F., Dairi, A., Sun, Y. (2020). Deep learning methods for fore- casting COVID-19 time-Series data: A Comparative study. Chaos, Solitons and Frac- tals, 140, 110121.

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