PREDICTING THE TRANSMISSION DYNAMICS OF TUBERCULOSIS VIA CAPUTO FRACTIONAL ORDER MODEL WITH NEURAL NETWOR | ||||
| Journal of Fractional Calculus and Applications | ||||
| Volume 16, Issue 1, 2025, Page 1-16 PDF (640.37 K) | ||||
| Document Type: Regular research papers | ||||
| DOI: 10.21608/jfca.2025.411050 | ||||
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| Abstract | ||||
| Abstract. This study presents the development of a hybrid Fractional Order Dierential Equation (FODE) and Articial Neural Network (ANN) model designed to predict the dynamics of Tuberculosis (TB) in Nigeria. The analysis utilized data sourced from the World Health Organization (WHO) TB Database and the Nigeria category, spanning the years 2010 to 2020. The Caputo derivative was used to formulate the fractional tuberculosis model which was enhanced with an ANN framework. The derived FODEs were discretized using the Gr¨unwald-Letnikov method for parameter estimation and numerical simulation of the TB data in MATLAB, employing varying memory values for the fractional-order model parameter 0 < α ≤ 1. To enhance predictive accuracy, we integrate an Articial Neural Network (ANN) with the FDE model, leveraging machine learning techniques for parameter estimation and forecasting. The ANN is trained using real-world TB data, employing the sigmoid function to represent time-dependent transmission rates. Our results demonstrate that the fractional-order model provides a more exible and accurate representation of TB dynamics compared to classical integer-order models. The proposed hybrid approach eectively captures disease trends, making it a valuable tool for epidemiological analysis and public health decision-making. | ||||
| Keywords | ||||
| Fractional Order Dierential Equation; Articial Neural Network; Tuberculosis (TB) Prediction | ||||
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