# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 1 Introduction  
An atypical coronavirus, COVID-19, was classified in Wuhan, China, in December 2019. As of 8 September 2021, the newly identified virus had become liable for hundreds of thousands of confirmed cases and over 4,610,056 deaths globally [1]. In addition to antiviral preventative methods such as wearing masks and avoiding crowds, the widespread distribution of SARS-CoV-2 vaccines is fundamental to mitigating viral spread as many vaccines have demonstrated an efficacy of over 95% in inhibiting SARS-CoV-2 symptoms. Around 545,624 COVID-19 infections and 8598 coronavirus-related casualties were disclosed KSA alone. Its swift and ceaseless transmission has induced a continuous pandemic, and as the number of cases increases, the urgent need to \"flatten the curve\" has become fundamental to evade the calamitous burdening of health institutions throughout the affected nations. Therefore, the critical need to elucidate the pathogenic mechanisms and epidemiological components of COVID-19 have led mathematical modeling and simulation to become the primary techniques for forecasting outbreaks. Predicting COVID-19 spread must be considered as it has the potential to aid the time-sensitive resolutions of healthcare personnel, strengthening the advancement of prevention strategies. In the literature, different mathematical epidemic surveillance models [2; 3; 4] have been presented related to COVID-19 diseases to forecast and predict disease occurrence patterns and trends under different situations. These patterns enable policies at the government level to stop the spread of disease and decrease the chance of infection. [PERSON] et al. [5] applied the Susceptible-Infectious-Recovered (SIR) model to forecast the SARS disease cases and deaths in Canada. Their finding estimated the fast spread of the SARS disease; every five days, the infection spread was about 1.5 to 3 per infection. [PERSON] et al. [6] employed a morecomprehensive mathematical model for Susceptible-Exposed-Infectious-Removed (SEIR) by considering new variables (a quarantined state with a recovery state) using the public data of NHC of COVID-19. Furthermore, for the present prognosis of disease flareups globally, clinical data from the Johns Hopkins dashboard has consistently held the attention of numerous modeling simulation experts [7]. For instance, [PERSON] et al. [8] reported that the primary reproduction number (R0; transmissibility of a virus), mortality, forecast per day of infection, and recovery rates are forecasted by employing the Susceptible-Infectious-Recovered-Dead (SIRD) model. In addition, the fatality and recovery case ratios were predicted with 90% confidence intervals, and the outbreak expansion was predicted for three weeks. Nevertheless, these models display much ambiguity in the long-term prediction of disease flareups due to the absence of necessary data. Thus, developing mechanisms using artificial intelligence (AI), such as the ML and DL approaches, is fundamental for analyzing clinical data during extended periods. As AI can help overcome the problems created due to the COVID-19 pandemic more efficiently by employing data mining techniques. AI exhibited a significant impact from different sides to tackle the COVID-19 issues. The system reviews show that the statistical and machine learning (ML) models have been widely used for various tasks such as COVID-19 forecasting, predicting, etc. Now, ML techniques are used worldwide for predictions because of their performance. However, machine learning (ML) techniques. However, a few challenges of using ML techniques are data availability, hyper tuning of parameters, and selecting the best ML model for prediction. Therefore, researchers have made predictions and chosen the best ML model based on the available datasets. Various ML models are mentioned in the literature, but the deep learning (DL) models give better performances for forecasting the COVID-19 cases [9].