NovelPredictionModel forCOVID-19 inSaudiArabiaBasedonan LSTM Algorithm

'e rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. 'is study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 preventionmeasures. A fully connected deep neural network, long short-termmemory (LSTM), and transformermodel were used as the AImodels for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia.'e performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million.'e results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. 'e findings of this study contribute to our understanding of COVID-19 containment. 'is study also provides insights into the prevention of future outbreaks.