# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 1 Introduction  
Coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged late in 2019 in Wuhan province in China as a series of cases with respiratory illnesses of unknown etiology at the time. Over a short period of time, the virus spread throughout the world leading to a pandemic of a magnitude that has not been seen in decades.  
From the early cases in late 2019 until the writing of this manuscript (28 th of June 2019), 10 million infected individuals and 500,000 deaths have been reported. More than 200 countries are affected, with major outbreaks in the United States (US), Brazil, Russia, and India [1]. The highest number of deaths are reported from the USA, Brazil, United Kingdom, Italy, and France [1]. Local policies have dictated individual countries responses, which ranged from a complete lockdown to mild restrictions in the daily activities of living. While not concrete, there seems to be a relationship between local policies and practices and the spread of the pandemic.  
In view of this, forecasting the number of effected cases using machine learning approaches might inform our understanding of disease trajectory. Our goal was to compare different machine learning approaches:  
(1) Purely data-drive approaches, such as linear regression [2; 3] and exponential regression that correlate the dependent variable (number of cases) to one independent variable (time) [4]. Regression models concentrate in predicting the future pattern of the dependent variables using the historical pattern detected over a specific period of time. The goal of the regression models is to capture all forms of regression modeling that assumes that the data based on time series is stationary; this means that if there is no reduction in the number of infected cases in the previous data, the model forecasts that the cases will keep increasing in constant level and steady matter throughout the entire series.  
(2) Partially data-driven: Susceptible-Infected-Recovered (SIR) Model, which is a type of infectious disease mathematical model, will assume that infected cases pattern will reach its peak at specific time and will decrease to reach zero case at some time in the future [5]. Using those two approaches leads to better understanding of the SARS-CoV-2 virus behavior and helps in showing the problem of dealing with uncertain situations. Considering the real-world scenarios in term of government policies, medical recommendations, and human behavior that are dynamic, it would not be wise to depend on one machine learning approach to evaluate the accuracy of the predicted model and to produce a singular answer to the question \"which machine learning approach should be used in the case of uncertain, dynamic situation similar to the situation that we are currently facing in COVID-19\"  
The paper is organized as follows: In Section 2, we summarize the related work. In Section 3, material and methods are explained. Section 4 covers the results, Section 5 compares the results, and Section 6 discusses the results with conclusion.