Datasets on the number of hospitalized, discharged, and death cases caused by Covid-19, and their prediction in Bushehr province, Iran

Published: 16 November 2021| Version 2 | DOI: 10.17632/bn2p7b5msn.2
Contributor:
Fatemeh Haghighat

Description

This dataset indicates the current and future trends of three indicators related to Covid-19 in Bushehr province, Iran. Figure 1 shows the daily trends of the number of hospitalized, discharged, and death cases caused by Covid-19 in Bushehr province between May 13, 2020, and April 1, 2021. As shown in this figure, all three indicators have seasonal and irregular changes. First, the number of discharged and death cases have been predicted according to their relationship with the number of hospitalized cases using the MLP neural network model. The structure of the proposed MLP model is shown in Figure 2. This model has been created using the neural network toolbox in MATLAB 15b software package, which also can generate model scripts. The script of the trained model is shown in supplementary .m File 1. Besides, Figure 3, Figure 4, and Figure 5 indicate the plots of performance, training state, and regression corresponding to the trained MLP neural network respectively. After predicting the test dataset using the MLP model through Simulink/MATLAB, the MC model has been used to improve the performance of the MLP model in prediction. The calculations related to the MC part have been performed in Excel 2010 software. The residual errors between the actual and predicted data by the MLP model for each indicator and the corresponding states are shown in Table 1. Figure 6 shows the forecast results of the next 40 days (April 2, 2021, to May 11, 2021) using the MLP-MC model for the two indicators of the number of discharged and death cases. Also, these values are shown in detail in Table 2.

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