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Modeling and Forecasting Cases of RSV Using Artificial Neural Networks

Author

Listed:
  • Myladis R. Cogollo

    (Departamento de Matemáticas y Estadística, Universidad de Córdoba, 230002 Montería, Colombia
    These authors contributed equally to this work.)

  • Gilberto González-Parra

    (Department of Mathematics, New Mexico Tech, Socorro, NM 87801, USA
    These authors contributed equally to this work.)

  • Abraham J. Arenas

    (Departamento de Matemáticas y Estadística, Universidad de Córdoba, 230002 Montería, Colombia
    These authors contributed equally to this work.)

Abstract

In this paper, we study and present a mathematical modeling approach based on artificial neural networks to forecast the number of cases of respiratory syncytial virus (RSV). The number of RSV-positive cases in most of the countries around the world present a seasonal-type behavior. We constructed and developed several multilayer perceptron (MLP) models that intend to appropriately forecast the number of cases of RSV, based on previous history. We compared our mathematical modeling approach with a classical statistical technique for the time-series, and we concluded that our results are more accurate. The dataset collected during 2005 to 2010 consisting of 312 weeks belongs to Bogotá D.C., Colombia. The adjusted MLP network that we constructed has a fairly high forecast accuracy. Finally, based on these computations, we recommend training the selected MLP model using 70% of the historical data of RSV-positive cases for training and 20% for validation in order to obtain more accurate results. These results are useful and provide scientific information for health authorities of Colombia to design suitable public health policies related to RSV.

Suggested Citation

  • Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2958-:d:683349
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    1. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    2. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    3. Lijing Yu & Lingling Zhou & Li Tan & Hongbo Jiang & Ying Wang & Sheng Wei & Shaofa Nie, 2014. "Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhe," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    4. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    5. Gwatkin, D.R., 1980. "How many die? A set of demographic estimates of the annual number of infant and child deaths in the world," American Journal of Public Health, American Public Health Association, vol. 70(12), pages 1286-1289.
    6. Fabio Della Rossa & Davide Salzano & Anna Di Meglio & Francesco De Lellis & Marco Coraggio & Carmela Calabrese & Agostino Guarino & Ricardo Cardona-Rivera & Pietro De Lellis & Davide Liuzza & Francesc, 2020. "A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    7. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    8. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 396-397, July.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    11. Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
    12. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    13. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
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