https://doi.org/10.1140/epjs/s11734-024-01274-4
Regular Article
The identification for time-varying parameter and noise of tuberculosis with seasonal changes based on data-behavior-driven system
1
School of Mathematics and Information Science, North Minzu University, 750021, Yinchuan, China
2
Ningxia Key Laboratory of Intelligent Information and Big Data Processing, 750021, Yinchuan, China
Received:
26
March
2024
Accepted:
18
July
2024
Published online:
5
August
2024
Time-varying and seasonal parameter inversion in the mathematical model of infectious diseases and uncertainty quantization based on actual data have great significance for real quantitative transmission process. In this study, the behavior-driven mathematical model of infectious diseases and the data-driven parameter identification method are combined to quantify the transmission law of tuberculosis (TB). To begin with, according to the characteristics of TB transmission, the TS-SID model with time-varying is established. Then, the improved identification algorithm is proposed to track the fluctuation of disease infection rate and mortality rate considering the seasonal influence. Meanwhile, focusing on the influence of noise on the spread of diseases, noise reduction and uncertain quantization are carried out on the data to identify the noise distribution. In addition, predict the denoised sequence and superimpose the noise distribution, which can improve the rationality of prediction. Finally, the numerical comparison shows that seasonal time-varying tracking is good for grasping and predicting the disease evolution.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
