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Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19

Author

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  • Beatriz González-Pérez

    (Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain
    Interdisciplinary Mathematics Institute (IMI), Complutense University of Madrid (UCM), 28040 Madrid, Spain)

  • Concepción Núñez

    (Laboratory of Research in Genetics of Complex Diseases, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain)

  • José L. Sánchez

    (Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain)

  • Gabriel Valverde

    (Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain)

  • José Manuel Velasco

    (Computer Architecture and Automation Department, Complutense University of Madrid (UCM), 28040 Madrid, Spain)

Abstract

We developed two models for real-time monitoring and forecasting of the evolution of the COVID-19 pandemic: a non-linear regression model and an error correction model. Our strategy allows us to detect pandemic peaks and make short- and long-term forecasts of the number of infected, deaths and people requiring hospitalization and intensive care. The non-linear regression model is implemented in an expert system that automatically allows the user to fit and forecast through a graphical interface. This system is equipped with a control procedure to detect trend changes and define the end of one wave and the beginning of another. Moreover, it depends on only four parameters per series that are easy to interpret and monitor along time for each variable. This feature enables us to study the effect of interventions over time in order to advise how to proceed in future outbreaks. The error correction model developed works with cointegration between series and has a great forecast capacity. Our system is prepared to work in parallel in all the Autonomous Communities of Spain. Moreover, our models are compared with a SIR model extension (SCIR) and several models of artificial intelligence.

Suggested Citation

  • Beatriz González-Pérez & Concepción Núñez & José L. Sánchez & Gabriel Valverde & José Manuel Velasco, 2021. "Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19," Mathematics, MDPI, vol. 9(13), pages 1-34, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1485-:d:581346
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    References listed on IDEAS

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    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Naudé, Wim, 2020. "Artificial Intelligence against COVID-19: An Early Review," IZA Discussion Papers 13110, Institute of Labor Economics (IZA).
    4. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
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    Cited by:

    1. Olga Krivorotko & Nikolay Zyatkov, 2024. "The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks," Mathematics, MDPI, vol. 12(19), pages 1-22, September.

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