| Abstract: |
| Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and out of these millions thousands of people become victims of this disease. In this work, the dynamics of malaria is analyzed using deep learning by implementing the SIR-SI compartment model. The main factor that controls disease transmission is the transmission rate and two of the most important factors that influence the transmission rate are temperature and altitude, thus in this work the transmission rate is analyzed with respect to temperature and altitude. We have performed the stability analysis of steady state solutions. After the mathematical analysis, Artificial neural network (ANN) was applied to the formulated model to predict the trajectory of all of the five compartments. As mentioned earlier the dynamics of disease are controlled by the parameters associated with the disease transmission and thus in this work three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) have been built to estimate these parameters from the trajectory of the data. |
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