Year: 2025
Type: article
Abstract: Malaria remains one of the leading causes of global morbidity and mortality, with millions of cases and fatalities annually. Effective intervention strategies by public health authorities and medical practitioners necessitate a robust understanding of disease transmission dynamics. This study presents a novel framework for modeling malaria transmission dynamics by integrating temperature and altitude-dependent transmission functions into a compartmental SIR-SI model. A key innovation lies in the introduction of a new transmission function that explicitly captures environmental dependencies, enhancing realism in the modeling of disease spread. We conduct steady-state analysis of the system, establishing the stability criteria for both disease-free and endemic equilibria through linearization techniques. We used a novel transmission function to model the dependence on temperature and altitude. To address the challenge of accurate parameter estimation, we develop a comparative learning framework using ANNs, RNNs, and PINNs, with PINNs standing out by embedding epidemiological dynamics into the training process. This enables physics-constrained parameter inference, significantly enhancing predictive performance over purely data-driven approaches. Additionally, we implement Dynamic Mode Decomposition (DMD) to derive a data-driven transmission risk index from infection trajectory data, providing a novel and interpretable metric for real-time risk assessment.
Source: Scientific Reports
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: United Arab Emirates University
Award: G6407613442
OpenAlex ID: W4412656065