🤖 AI Summary
Malaria transmission is strongly influenced by temperature and elevation, yet conventional models struggle to quantify dynamic spatiotemporal risk. Method: We develop an environment-integrated compartmental dynamical model, employing equilibrium analysis and Lyapunov stability criteria to characterize the basic reproduction threshold. We further introduce an artificial neural network (ANN) to forecast infection trajectories across five population groups; jointly deploy convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for key parameter inversion; and—novelly—apply dynamic mode decomposition (DMD) to quantify malaria risk in space and time. Contribution/Results: The framework achieves high-accuracy trajectory prediction (MAE < 2.3%), precisely identifies high-risk regions and critical transmission conditions, and delivers an interpretable, computationally tractable quantitative foundation for environmental-driven infectious disease control and policy-making.
📝 Abstract
Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.