Abstract
Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.
Highlights
With the spread of the unfamiliar Coronavirus (COVID-19), which was first discovered in Wuhan city in China in 2019, societies worldwide continue to face very distressing times
Data from January 30, 2020 to August 16, 2020, were analyzed, with 75% data employed for practice and 25% for predictive and validation purposes
COVID-19 is an ongoing pandemic that significantly endangers the health of people worldwide in a short period
Summary
With the spread of the unfamiliar Coronavirus (COVID-19), which was first discovered in Wuhan city in China in 2019, societies worldwide continue to face very distressing times. The most frequently used conventional pandemic schemes are susceptible—infected—recovered (SIR), and susceptible— exposed—infected—recovered (SEIR) models (4), where “S,” “E,” “I,” and “R” signify every number of susceptive persons, the magnitude of individuals during the incubation phase, the magnitude of contagious persons and the number of individuals improved, respectively These models are trained to forecast multiple diseases, such as Ebola and SARS, due to their robust predictive abilities of the linked indications. Traditional disease models measure the rate of infection based on the complex variation in the number of contaminations and determine the disease’s spread and evolution pattern Those approaches assume that all individuals with Coronavirus hold an equal chance of infection, and their predictive results can only suggest general patterns and are restricted. This work investigates the modified LSTM approach to forecasting the likely COVID-19 cases and deaths It describes deep reinforcement learning for optimizing the prediction results based on symptoms.
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