# An Integrated Neural Network and SEIR Model to Predict COVID-19
###### Abstract  
A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the \(R_{0}\)-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated by showing the difference in accuracy between the integrated model and the general SEIR model. The result shows that the integrated model is more accurate than the general SEIR model while predicting the number of confirmed cases in Bangladesh.  
COVID-19; coronavirus; SARS-CoV-2; 2019-nCoV; SEIR; neural network; basic reproduction number +
Footnote †: journal: ApJ  
0000-0001-99; 0000-0001-9998-0000]M. A.  
administrative unit of Bangladesh. Eventually, such a mobile application will help decision-makers to develop an appropriate policy to control the impact of COVID-19 in areas of Bangladesh. The reason for this is that the developed mobile app will help decision-makers visualize the total confirmed cases as well as to simulate the impact of an intervention by taking into account custom data for confirmed cases of a specific area.  
In the SEIR model, the total population of a region is divided into four compartments. The persons, who are in danger of being infected take place in the susceptible compartment. When a person from the susceptible portion comes into contact with an infected person, that person is moved to the exposed compartment. When the person is infected by the virus, he or she will take a position in the infected compartment. After being in the infected area, the person may get well or may leave their last breath. Hence, he or she will move to the removed compartment. In contrast, in the SIR (Susceptible, Infected, Removed) model, the exposed compartment is ignored. The person of a susceptible compartment who made contact with an infected person is directly moved to the infected compartment. Hence, it can be seen that the SEIR model consists of four compartments while the SIR model consists of three compartments.  
A neural network [5] uses a backpropagation [6] algorithm, which is also known as a feedback process. The difference between the predicted output of the network and the actual output is used to update the weights of each unit of the network. The network of our integrated framework is trained to calculate the number of quarantined persons in a day. Moreover, the daily total confirmed cases as well as the basic reproduction rate (R\({}_{0}\)) of any region of Bangladesh can also be calculated using this model.  
The remainder of the paper is structured as follows. Section 2 presents the related works. Section 3 describes the methodology of this research. The framework of SEIR_NN-based apps development is described in Section 4, and the process of this experiment is given in Section 5. Finally, Sections 6 and 7 present an analysis of the results and conclude the paper, with a brief discussion on the future scopes in improving upon the performance achieved.