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Advances in Distributed Computing and Machine Learning pp 328–339Cite as

Artificial Neural Network Modeling for Prediction of Coronavirus (COVID-19)

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 302))

Abstract

A public health emergency of international importance is COVID-19 (coronavirus disease 2019). There is no known successful medicinal treatment at this time, although the extreme type of the disease is very important for patients. In data analysis and prediction, artificial neural networks are a common method. The research work aims to create a model of the neural network that predicts the Coronavirus's spread. The Corona Virus Disease 2019 (COVID-19) transmission method using ANN is described in this paper. Symptoms will increase from 2 days to 2 weeks during contact with the virus. A pooled analysis of 181 COVID-19 confirmed cases within Wuhan, China, reveals that the maximum infection rate was 5.1 days and 97.5% of individuals who received treatment did so within 11.5 days of illness. ANN is used to predict the spread of Coronavirus. Also as a response, in the training process, 99% precision is achieved. With empirical correlations, the precision of the established comparison of the model was made. It was a wonderful match that has been found between the network of neurons and real data. With the data from the experiments, a database was created, dividing them into training groups (80%) and tests (20%). The models were obtained using the Python program. The efficiency of the neural network models was assessed by the coefficient of determination (R2) and error-index (MSE). The model built from ANN and Long short-term memory (LSTM) modeling accurately predicted the outcome satisfactorily.

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Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, Gandhi Institute For Technology, Bhubaneswar, Odisha, India

    Saroja Kumar Rout & Bibhuprasad Sahu

  2. Department of Computer Science & Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha, India

    Bikram keshari Mishra

  3. Department of CSIT, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Debabrata Singh

Authors
  1. Saroja Kumar Rout
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  2. Bibhuprasad Sahu
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  3. Bikram keshari Mishra
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  4. Debabrata Singh
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Technology, Institute of Technical Education and Research(ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  2. School of Information Technology and Engineering (SITE), Vellore Institute of Technology, Vellore, India

    Asis Kumar Tripathy

  3. Center for Forensic Science, School of Mathematical and Physical Science, University of Technology Sydney, Broadway, NSW, Australia

    Manoranjan Mohanty

  4. Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Department. of Computer Science and Information Technology, Institute of Technical Education and Research(ITER), Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India

    Ajit Kumar Nayak

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Rout, S.K., Sahu, B., Mishra, B.k., Singh, D. (2022). Artificial Neural Network Modeling for Prediction of Coronavirus (COVID-19). In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_32

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