Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model

Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model.