Abstract
The 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. This 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 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The 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. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks.
Highlights
Emerging and reemerging viruses pose severe challenges to public health
E variable transmission of SARS-CoV-2 raises the question “what are the factors influencing the spread of the virus in Saudi Arabia?” Saudi Arabia was one of the first countries to react and demonstrated innovative leadership characteristics when dealing with COVID-19
We introduce a comparative analysis of three different deep learning time-series prediction models for predicting COVID-19 cases for up to the subsequent five days
Summary
Emerging and reemerging viruses pose severe challenges to public health. Coronaviruses are a family of highly pathogenic enveloped RNA viruses, which are widely transmitted among humans [1]. In late December 2019, a new coronavirus pandemic erupted unexpectedly in Wuhan, China, posing a serious threat to everyday human life. E new virus, dubbed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the World Health Organization [2], causes coronavirus disease (COVID-19), which often results in death [1]. E rate of spread within a country is determined by factors such as weather conditions, city population density, level of urbanization, social cohesiveness, and cultural factors that can be identified as influential factors of human-tohuman transmission of the virus. E variable transmission of SARS-CoV-2 raises the question “what are the factors influencing the spread of the virus in Saudi Arabia?” Saudi Arabia was one of the first countries to react and demonstrated innovative leadership characteristics when dealing with COVID-19.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
