International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 184 - Number 2 |
Year of Publication: 2022 |
Authors: Manu Tyagi, Peeyush Kumar, Mayank Agarwal, Unnati Gangwar, Jayati Bhardwaj, Priyanshu Murari |
10.5120/ijca2022921979 |
Manu Tyagi, Peeyush Kumar, Mayank Agarwal, Unnati Gangwar, Jayati Bhardwaj, Priyanshu Murari . COVID-19 Outbreak Prediction using Artificial Neural Network: A Review. International Journal of Computer Applications. 184, 2 ( Mar 2022), 48-51. DOI=10.5120/ijca2022921979
The global spread of the COVID-19 outbreak has led to studies on a variety of topics, including predictions of predictable cases. Because it helps to identify the need to deal with epidemic situations. We used artificial neural networks (ANNs) in this study to predict the number of COVID-19 cases in Brazil, Mexico, India, and Italy in the coming days. The Prey Predator Method (PPA) is a type of meta-heuristic algorithm for training guessing models. The root function of the mean squared error (RMSE) and the correlation coefficient were used to evaluate the performance of the target ANN models (R). ANN models performed much better than other models in Brazil, Mexico, India, and Italy in terms of disease rates (active cases), recovery, and death. The simulation results for the ANN models predict the values accurately. Traditional monolithic neural networks have a much higher percentage of predictor errors than meta-heuristic algorithms. The report shows an estimated daily morbidity, recovery and mortality in Brazil, Mexico, India and Italy in early 2021.