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The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks

Author

Listed:
  • Olga Krivorotko

    (Sobolev Institute of Mathematics SB RAS, Akademician Koptuyg Ave. 4, 630090 Novosibirsk, Russia
    These authors contributed equally to this work.)

  • Nikolay Zyatkov

    (Sobolev Institute of Mathematics SB RAS, Akademician Koptuyg Ave. 4, 630090 Novosibirsk, Russia
    These authors contributed equally to this work.)

Abstract

New epidemics encourage the development of new mathematical models of the spread and forecasting of infectious diseases. Statistical epidemiology data are characterized by incomplete and inexact time series, which leads to an unstable and non-unique forecasting of infectious diseases. In this paper, a model of a conditional generative adversarial neural network (CGAN) for modeling and forecasting COVID-19 in St. Petersburg is constructed. It takes 20 processed historical statistics as a condition and is based on the solution of the minimax problem. The CGAN builds a short-term forecast of the number of newly diagnosed COVID-19 cases in the region for 5 days ahead. The CGAN approach allows modeling the distribution of statistical data, which allows obtaining the required amount of training data from the resulting distribution. When comparing the forecasting results with the classical differential SEIR-HCD model and a recurrent neural network with the same input parameters, it was shown that the forecast errors of all three models are in the same range. It is shown that the prediction error of the bagging model based on three models is lower than the results of each model separately.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3044-:d:1488221
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    References listed on IDEAS

    as
    1. Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. 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.
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