# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 2 Related Work  
This segment contains a review of data analyses that have investigated COVID-19 transmission trends. Various forecasting techniques (statistical, analytical, and mathematical) have been discussed in the literature by applying different data sources for COVID-19 analysis. In addition, they considered different parameters of the existing dataset. In past pandemics, researchers have applied traditional mathematical approaches [9] to forecast the spread of disease and mortality rate in a specified period. These traditional methods were effective and gave a better performance in terms of forecasting. Due to this reason, in the current COVID-19 pandemic, most researchers have used these traditional mathematical models to predict the spread and mortality rates [10; 11; 12; 13]. For example, [PERSON] [14] used the well-known and simple mathematical model known as the SIR model in his study to find the pattern of COVID-19. Their finding show that countries that have taken concrete measures and other facilities (quarantine, etc.) to stop the spread of COVID-19 disease have a positive impact on the mortality rate. However, this mathematical model shows a lower performance due to the dynamic nature of the COVID-19 dataset. [PERSON] et al. [15] introduced [PERSON]'s function-based analysis. Their finding shows that this model gives better forecasting about the spread of disease and helps the government body take proper actions at the right time. [PERSON] et al. [16] applied mathematical modeling using age and gender parameters on mortality rate. The COVID-19 virus mainly affects the elderly age group (above the age of 65). Therefore, policies such as quarantine and social distancing can assist in stopping the spread of the virus. GLEaM, a computational model for visualizing viral spread ([PERSON] et al. [8]), can effectively evaluate lifelike scenarios compared to data. As the modeling system's development progresses, transportation and interaction layers depend on newly evolving epidemic strains. The GLEaM model capitalizes on one's portability to construct epidemic simulations through the assimilation of unlike processes. Moreover, health personnel may reveal clinical data much later, impeding epidemiologic progression. Real-time data contained in informal media permit the advancement of epidemic forecasts. For example, data from Twitter and HealthMap for Haitian cholera outbreaks were analyzed for the first 100 days of 2010 and compared to government-authorized case statistics. The trends of two-week cases of both unofficial data origins correlated with the official case [17]. Similarly, in the example of Hurricane Tomas, guesses of the reproductive numbers were found to range from 1.04 to 1.51 (informal) and 1.06 to 1.73 (official) [18]. Thus, fundamentally comparable data were achieved significantly earlier than the dispensation of official case counts. Therefore, unofficial media can be utilized as a cost-effective point of supply for prompt assessments of fluctuations in transmission trends. In addition, the Susceptible-Exposed-Infected-Recovered (SEIR) models were employed to compose a simulation inquiry to examine viral spread tendencies and predict new cases in 51 countries. Epidemiological parameters separated the nations into four aggregations that represented the following risk levels: high risk, medium risk, low risk, and efficacious control. Likewise, Approximate Bayesian Computation (ABC) algorithms can also forecast infectious disease trends [19]. Three viral spread case studies highlight the user-based components that heighten processing rates and reliability. Furthermore, [PERSON] et al. suggested a method of providing swift and authentic epidemic forecasts for public health officials in India. Although COVID-19 transmission has been thoroughly examined, most related algorithms are either too computationally expensive, too granular to be dependable, or too fine-grained to be competent.