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

Olga Krivorotko () and Nikolay Zyatkov
Additional contact information
Olga Krivorotko: Sobolev Institute of Mathematics SB RAS, Akademician Koptuyg Ave. 4, 630090 Novosibirsk, Russia
Nikolay Zyatkov: Sobolev Institute of Mathematics SB RAS, Akademician Koptuyg Ave. 4, 630090 Novosibirsk, Russia

Mathematics, 2024, vol. 12, issue 19, 1-22

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.

Keywords: generative adversarial networks; conditional GANs; regularization; deep learning; time series; COVID-19; forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/19/3044/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/19/3044/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:19:p:3044-:d:1488221

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().