Analysis of a mathematical model for malaria using data-driven approach
This work addresses malaria prediction for medical professionals and governments, but it is incremental as it combines existing modeling and machine learning techniques without major breakthroughs.
The authors tackled the problem of understanding malaria dynamics by proposing a compartmental model dependent on temperature and altitude, and applied neural networks to predict disease trajectories and calculate risk using dynamic mode decomposition, achieving predictions across five compartments with three different architectures.
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.