Giardiasis transmission dynamics: insights from fractal-fractional modeling and deep neural networks

The World Health Organization highlights Giardias as a neglected zoonotic disease caused by Giardia duodenalis. The disease often goes overlooked despite the significant harm it causes humans and animals. We present a mathematical model for transmitting Giardiasis incorporating various preventative measures, including screening, treatment, and environmental sanitation. Among the factors influencing Giardiasis transmission within a community is the interaction parameter between humans and the environment. In this manuscript, Atangana-Baleanu Caputo (ABC) derivatives of fractional order v and fractal dimension q are utilized to explore a modified model with a fractal-fractional approach. The study qualitatively analyses the model using functional non-linearity and population-based fixed-point theory. The fractional Adams-Bashforth iterative method is used to obtain numerical solutions. Ulam-Hyers (UH) stability techniques are used to analyze stability in this study. A comparison is made between simulation results for all compartments and Giardia duodenalis data already available. To manage Giardiasis duodenalis effectively, societal behavioral changes and adherence to preventive measures are essential to controlling the effective transmission rate. Additionally, a deep neural network (DNN) approach is used to analyze the given disease condition with excellent accuracy in training, testing, and validation data.