# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 2 Related Work  
Since the worldwide spread of COVID-19, researchers have engaged in intensive efforts to use scientific models and tools to predict the pandemic numbers in order to help countries understand and better manage its spread. These methods relied on variety of models, including data science, machine learning, statistical models, and SEIR models. In this section, we summarize the studies that were recently published. The work in [6] proposed an iterative weighting for fitting Generalized Inverse Weibull distribution model to analyze and predict the growth of the COVID-19 epidemic. They applied the model using cloud computing to predict the potential threat of COVID-19 in countries worldwide in real-time. The authors in [7] examined the transmission characteristics of the COVID-19 epidemic at different stages using Gaussian distribution theory to construct a new model of coronavirus transmission. By simulating the propagation process of the COVID-19, we found that the curves of the proposed model well simulate the official data curves of Hubei, Non-Hubei area of China, and South Korea, Italy, and Iran. The authors reported key factors that affect the spread of the virus; these include the basic reproduction number, virus incubation period, and daily infection number. In [8], mathematical and numerical analysis is used to come up with reliable and accurate predictions of the COVID-19 pandemic in Pakistan. The time-dependent SIR model was used to provide future predictions. The turning point of the peak of the pandemic is defined as the day when the transmission rate becomes less than the recovering rate. They predicted that the outbreak will reach its maximum peak occurring from late May to 9 th of June 2020. Their model predicted that after the peak date, the infection rate will start decreasing, but it might take months for the pandemic to completely fade away with 97% recovery happening in late August-to-September 2020. In [9], machine learning models are used to forecast the number of people affected by COVID-19. The researchers used four standard forecasting models; these are: linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) to forecast the threatening factors of COVID-19. The results show that the ES performs best among all the used models, followed by LR and LASSO, which perform well in forecasting the new confirmed cases, death rate and recovery rate, while SVMperforms poorly in all the prediction scenarios given the available dataset. In [10], a comparative analysis of machine learning and soft computing models is used to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). The study concludes that machine learning models are effective in predicting the pandemic. The study proposes to integrate machine learning and SEIR models to real novelty in the pandemic prediction. In [11], the researchers developed a \"Corona Community Tracker\" using a Susceptible-Exposed-Infected-Removed (SEIR) system and used it to predict the COVID-19 pandemic in China. The authors assert that the SEIR model may help to interpret patterns of public sentiment on disseminating related health information, and assess political and economic influence of the spread of the virus. In [12], a parsimonious model that uses both quarantine of symptomatic infected individuals is proposed, as well as population-wide isolation practices in response to containment policies or behavioral changes. They show that the model explains the observed growth behavior accurately. The insights provided from the application of the model may be helpful in the implementation of containment strategies of COVID-19.