Outbreak prediction of COVID-19 using Recurrent neural network using Wireless Sensor Network
Vinod Kumar, Ashish Dixit, Mukta Makhija, Jaishree Jain, Nishant Anand, Mridula Dwivedi · Procedia Computer Science · 2024
A multitude of outbreak prediction models are employed on a global scale to facilitate informed decision-making and the implementation of appropriate control measures in light of the COVID-19 pandemic. The authorities are now focusing on a straightforward epidemiological and statistical methodology for forecasting the global COVID-19 epidemic. These models are widely recognized within the media landscape. Long-term prediction has been found to have poor precision in typical models due to the presence of considerable uncertainty and insufficient vital data. Despite several efforts that have been undertaken to tackle this issue, there is a necessity to enhance the fundamental generalization and robustness of current models. There is a need for a comparative analysis of recurrent neural networks (RNN) and long short-term memory models (LSTM) in order to forecast the breakout of COVID-19, in comparison to the currently existing models. The architectural design incorporates the concept of the attention mechanism, specifically, the use of attention inside hidden data. This methodology enables the identification of patterns and the prioritization of time-sensitive information, leading to the creation of a network that is easily comprehensible. This research presents a proposal for utilizing RNN with LSTM to anticipate outbreaks of COVID-19. Additionally, we have incorporated a learning vector time embedding into our methodology, as the inclusion of time in a vector representation may be seamlessly integrated into many architectures. The model provides a forecast of the progression of the COVID-19 pandemic in Europe’s most severely affected nations, including Italy, France, Spain, as well as a country in North America, specifically Canada. This study proposes that variations in behavior across different nations indicate the potential efficacy of employing machine learning as a modelling tool for understanding and predicting the spread of epidemics. This paper provides an initial benchmarking analysis to demonstrate the potential of recurrent neural networks (RNNs) for future academic investigations. The study presents a unique method that combines RNNs with LSTM to effectively forecast flare-ups. The experimental results show that our proposed LSTM model performs better than existing models with lesser RMSE and MAPE values.