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Augmented Artificial Neural Network Model for the COVID-19 Mortality Prediction: Preliminary Analysis of Vaccination in Turkey

Year 2022, Volume: 5 Issue: 1, 22 - 36, 30.04.2022

Sena Kır Elif Elçin Günay

Abstract

The spread and severity of coronavirus disease 2019 (COVID-19) have a severe impact on our lives, so that over 4.6 million lives have been lost since it has been first emerged. Although prediction of the COVID-19 mortality may be inevitably accompanied by uncertainty, it is helpful for health politicians and public health decision-makers to take proper precautions to diminish the pandemic's severity. Therefore, this study proposed a mortality prediction model for the deaths that occur on-day, lag 1 day, lag 7 day, and lag 14 day in Turkey, considering 16 variables under four categories as follows: (i) severity of the disease, (ii) vaccination policy as a preventive strategy, (iii) exposure duration in society, (iv) time series impact. The developed Augmented- Artificial Neural Network (ANN) model took advantage of Auto-Regressive Integrated Moving Average (ARIMA) and ANN models to capture the linear and nonlinear components of the mortality. The proposed model was able to predict mortality with the lowest error compared to ARIMA and ANN models. To reveal the impact of each responsible category on mortality, a set of experiments was designed. According to the experiments' results, it was observed that the impact of four categories from highest to the lowest importance on prediction performance were exposure duration in society, vaccination policy, severity of disease, and time series, respectively. According to these results, new virus-fighting policies can be developed, and the existing model can be used as a simulation tool with the new data to be obtained.

Keywords

ANN ARIMA coronavirus vaccination estimation

References

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  • I. Ahmad and S. M. Asad, “Predictions of coronavirus COVID-19 distinct cases in Pakistan through an artificial neural network,” Epidemiology and Infection, vol. 148, 2020.
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  • S. Arslan, M. Y. Ozdemir, and A. Ucar, “Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study,” Front. Public Health, vol. 8, 2020.
  • R. Salgotra, M. Gandomi, and A. H. Gandomi, “Time series analysis and forecast of the COVID-19 pandemic in India using genetic programming,” Chaos, Solitons & Fractals, vol. 138, 109945, 2020.
  • A. I. Saba and A. H. Elsheikh, “Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks,” Process Saf. Environ. Protection, vol. 141, pp. 1-8, 2020.
  • K. C. Santosh, “COVID-19 prediction models and unexploited data,” J. Med. Sys., vol. 44, no. 170, 2020.
  • O. Sharma, A. A. Sultan, H. Ding, and C. R. Triggle, “A Review of the Progress and Challenges of Developing a Vaccine for COVID-19,” Frontiers in Immunology, vol. 11, 2413, 2020.
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  • P. Chrusciel and S. Szybka, “On the lag between deaths and infections in the first phase of the COVID-19 pandemic,” medRxiv, 2021.
  • G. Grasselli, et al., 1Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy,” Jama, vol. 323, no. 16, pp. 1574-1581, 2020.
  • S. Morid, and V. Smakhtin, “Drought forecasting using artificial neural networks and time series of drought indices,” Int. J. Climatol., vol. 27, no. 15, pp. 2103-2111, 2007.
  • X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799-811, 2019.
  • G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
  • M. D. Philemon, Z. Ismail, and J. Dare, “A review of epidemic forecasting using artificial neural networks,” Int. J. Epidemiol. Res., vol. 6, no. 3, pp. 132-143, 2019.
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  • A. A. Suratgar, M. B. Tavakoli, A. Hoseinabadi, “Modified Levenberg–Marquardt method for neural networks training,” World Acad. Sci. Eng. Technol., vol. 6, no. 1, pp. 46-48, 2005.
  • H. Yu, and B. M. Wilamowski, “Levenberg-Marquardt training,” Industrial Electronics Handbook, vol. 5, no. 12, 1, 2011. N. Andrei, “Scaled conjugate gradient algorithms for unconstrained optimization,” Comput. Optim. Appl., vol. 38, no. 3, pp. 401-416, 2007.
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  • G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003.
  • S. I. Alzahrani, I. A. Aljamaan, and E. A. Al-Fakih, “Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions,” J. Inf. and Public Health, vol. 13, no. 7, pp. 914-919, 2020.

Year 2022, Volume: 5 Issue: 1, 22 - 36, 30.04.2022

Sena Kır Elif Elçin Günay

Abstract

References

  • WHO, “World Health Organization COVID-19 Dashboard,” 2021. [Online] Available: https://covid19.who.int/ [Accessed: 01-Sep-2021]
  • A. Hernandez-Matamoros, H. Fujita, T. Hayashi, and H. Perez-Meana, “Forecasting of COVID-19 per regions using ARIMA models and polynomial functions,” Appl. Soft Comp., vol. 96, 106610, 2020.
  • S. Zhang, M. Diao, W. Yu, L. Pei, Z. Lin, and D. Chen, “ International Journal of Infectious Diseases Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: a data-driven analysis,” Int. J. Infect. Dis., vol. 93, pp. 201–204, 2020.
  • R. Pal, A. A. Sekh, S. Kar, and D. K. Prasad, “ Neural network based country wise risk prediction of COVID-19,” Appl. Sci., vol. 10, no. 18, 6448, 2020.
  • Z. Ceylan, “Estimation of COVID-19 prevalence in Italy, Spain, and France,” Sci. Total Environ., vol. 729, 138817, 2020.
  • M. Khashei and M. Bijari, “A novel hybridization of artificial neural networks and ARIMA models for time series forecasting,” Appl. Soft Comp., vol. 11, no. 2, pp. 2664-2675, 2011.
  • A. Mollalo, K. M. Rivera, and B. Vahedi, “ Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States,” Int. J. Environ. Res. Public Health, vol. 17, no. 12, 4204, 2020.
  • I. E. Agbehadji, B. O. Awuzie, A. B. Ngowi, and R. C. Millham, “Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing,” Int. J. Environ. Res. and Public Health, vol. 17, no. 5, 5330, 2020.
  • F. N Khan, A. A. Khanam, A. Ramlal, and S. Ahmad, A review on predictive systems and data models for COVID-19. In Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Springer, Singapore, pp. 123-164, 2021.
  • S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for COVID-19 (SARS-CoV-2) pandemic: A review,” Chaos, Solitons & Fractals, vol. 139, 110059, 2020.
  • Y. Mohamadou, A. Halidou, P. T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19,” Appl. Intell., vol. 50, no. 1, pp. 3913-3925, 2020.
  • I. Rahimi, F. Chen, A. H. Gandomi, “ A review on COVID-19 forecasting models,” Neural Comput. and Applic., pp. 1-11, 2021.
  • H. Swapnarekha, H. S. Behera, J. Nayak, and B. Naik, “ Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review,” Chaos, Solitons & Fractals, vol. 138, 109947, 2020.
  • L. Peng, W. Yang, D. Zhang, C. Zhuge, and L. Hong, “ Epidemic analysis of COVID-19 in China by dynamical modeling," arXiv preprint arXiv:2002.06563, 2020.
  • F. M. Khan and R. Gupta, “ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India,” J. Saf. Sci. Resilience, vol. 1, no. 1, pp. 12-18, 2020.
  • Z. Malki, E. S. Atlam, A. E. Hassanien, G. Dagnew, M. A. Elhosseini, and I. Gad, “Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches,” Chaos, Solitons and Fractals, vol. 138, 110137, 2020.
  • M. A. Achterberg, B. Prasse, L. Ma, S. Trajanovski, M. Kitsak, and P. Van Mieghem, “Comparing the accuracy of several network-based COVID-19 prediction algorithms,” Int. J. Forecasting, In Press.
  • S. Dhamodharavadhani, R. Rathipriya, and J. M. Chatterjee, “COVID-19 mortality rate prediction for India using statistical neural network models,” Front. Public Health, vol. 8, 2020.
  • G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, R. Gloaguen, “COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach,” Mathematics, vol. 8, no. 6, 890, 2020.
  • L. Moftakhar, S.E.I.F., Mozhgan, and M. S. Safe, “Exponentially increasing trend of infected patients with COVID-19 in Iran: a comparison of neural network and ARIMA forecasting models,” Iranian Journal of Public Health, 49, 2020.
  • O. Torrealba-Rodriguez, R. A. Conde-Gutiérrez, and A. L. Hernández-Javier, “Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models,” Chaos, Solitons and Fractals, 138, 109946, 2020.
  • P. Arora, H. Kumar, and B. K. Panigrahi, “Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India,” Chaos, Solitons and Fractals, vol. 139, 110017, 2020.
  • C. P. Kuo and J. S. Fu, “Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions,” Sci. Total Environ., vol. 758, 144151, 2021.
  • I. Ahmad and S. M. Asad, “Predictions of coronavirus COVID-19 distinct cases in Pakistan through an artificial neural network,” Epidemiology and Infection, vol. 148, 2020.
  • M. A. Al-qaness, A.A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” J. Clin. Med., vol. 9, no. 3, 674, 2020.
  • A. K. Sahai, N. Rath, V. Sood, and M. P. Singh, “ARIMA modelling & forecasting of COVID-19 in top five affected countries,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 5, pp. 1419-1427, 2020.
  • L. Bayyurt and B. Bayyurt, “Forecasting of COVID-19 cases and deaths using ARIMA models,” Medrxiv, 2020.
  • I. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons and Fractals, 138, 110015, 2020.
  • M. Niazkar, T. G. Eryılmaz, H. R. Niazkar, and Y. A. Türkkan, “Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey,” Comp. Math. Methods in Med., 2020.
  • S. Arslan, M. Y. Ozdemir, and A. Ucar, “Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study,” Front. Public Health, vol. 8, 2020.
  • R. Salgotra, M. Gandomi, and A. H. Gandomi, “Time series analysis and forecast of the COVID-19 pandemic in India using genetic programming,” Chaos, Solitons & Fractals, vol. 138, 109945, 2020.
  • A. I. Saba and A. H. Elsheikh, “Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks,” Process Saf. Environ. Protection, vol. 141, pp. 1-8, 2020.
  • K. C. Santosh, “COVID-19 prediction models and unexploited data,” J. Med. Sys., vol. 44, no. 170, 2020.
  • O. Sharma, A. A. Sultan, H. Ding, and C. R. Triggle, “A Review of the Progress and Challenges of Developing a Vaccine for COVID-19,” Frontiers in Immunology, vol. 11, 2413, 2020.
  • F. Jung, V. Krieger, F. T. Hufert, and J. H. Küpper, “Herd immunity or suppression strategy to combat COVID-19,” Clinical Hemorheology and Microcirculation, pp. 1-5, 2020.
  • Our World in Data, “Statistics and Research: Coronavirus (COVID-19) Vaccinations,” 2021. [Online] Available: https://ourworldindata.org/covid-vaccinations [Accessed: 09-Sep-2021]
  • Google, “Community Mobility Reports,” 2021. [Online] Available: https://support.google.com/covid19-mobility/answer/9824897?hl=en [Accessed: 20-Jul-2021]
  • F. Pan, et al., “ Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): A case-control study,” Int. J. Med. Sci., vol. 17, no. 9, 1281, 2020.
  • Turkish Republic Ministry of Health, “COVID-19 Information Platform,” 2021. [Online] Available: https://covid19.saglik.gov.tr/TR-66935/genel-koronavirus-tablosu.html [Accessed: 20-Jul-2021]
  • Our World in Data, “COVID-19 Vaccinations Public Data: Turkey,” 2021b. [Online] Available: https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/country_data/Turkey.csv [Accessed: 20-Jul-2021]
  • P. Chrusciel and S. Szybka, “On the lag between deaths and infections in the first phase of the COVID-19 pandemic,” medRxiv, 2021.
  • G. Grasselli, et al., 1Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy,” Jama, vol. 323, no. 16, pp. 1574-1581, 2020.
  • S. Morid, and V. Smakhtin, “Drought forecasting using artificial neural networks and time series of drought indices,” Int. J. Climatol., vol. 27, no. 15, pp. 2103-2111, 2007.
  • X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799-811, 2019.
  • G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
  • M. D. Philemon, Z. Ismail, and J. Dare, “A review of epidemic forecasting using artificial neural networks,” Int. J. Epidemiol. Res., vol. 6, no. 3, pp. 132-143, 2019.
  • G. Cybenko, “Approximations by superpositions of a sigmoidal function,” Math. Control Signal Systems, vol. 2, pp. 303–314, 1989.
  • K. Hornik, M. Stinchcombe, and H. White, “Multilayer feed forward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989.
  • G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: the state of the art,” Int. J. Forecasting, vol. 14, no. 1, pp. 35–62, 1998.
  • R. M. Rizk-Allah, and A. E. Hassanien, “COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network,” arXiv preprint arXiv:2004.05960, 2020.
  • A. A. Suratgar, M. B. Tavakoli, A. Hoseinabadi, “Modified Levenberg–Marquardt method for neural networks training,” World Acad. Sci. Eng. Technol., vol. 6, no. 1, pp. 46-48, 2005.
  • H. Yu, and B. M. Wilamowski, “Levenberg-Marquardt training,” Industrial Electronics Handbook, vol. 5, no. 12, 1, 2011. N. Andrei, “Scaled conjugate gradient algorithms for unconstrained optimization,” Comput. Optim. Appl., vol. 38, no. 3, pp. 401-416, 2007.
  • M. T. Hagan, H. B. Demuth, and M. Beale, Neural network design. PWS Publishing Co., 1997.
  • G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003.
  • S. I. Alzahrani, I. A. Aljamaan, and E. A. Al-Fakih, “Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions,” J. Inf. and Public Health, vol. 13, no. 7, pp. 914-919, 2020.
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Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Sena Kır 0000-0002-5615-8814

Elif Elçin Günay 0000-0002-4464-8779

Publication Date April 30, 2022
Submission Date September 22, 2021
Acceptance Date January 26, 2022
Published in Issue Year 2022Volume: 5 Issue: 1

Cite

IEEE S. Kır and E. E. Günay, “Augmented Artificial Neural Network Model for the COVID-19 Mortality Prediction: Preliminary Analysis of Vaccination in Turkey”, SAUCIS, vol. 5, no. 1, pp. 22–36, 2022.
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Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science      28938