# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 3 Methods and Datasets
### Dataset Preprocessing  
The dataset was inspected and several data-preprocessing techniques were applied prior to the analysis. For example, it was noticed that the Date and Day attributes both indicate the same thing, with the only difference being that the former is an absolute value while the latter is a relative value. To avoid redundant information in the dataset, the Date attribute was excluded from the analysis. Additionally, the attributes One Dose and Fully Vaccinated were dropped because they are just cumulative percentages that show an overall picture of the vaccination drive conducted in the UAE but do not show daily information that could affect the trends directly.  
For the event-based prediction, three additional features are manually added to the dataset, namely Outdoor Events, Policies, and National Holidays. These three attributes are of type categorical Boolean, with a value of 1 when there was an event e.g., when it was a national holiday, when a new policy was put in place, when a major outdoor event was held involving crowds, etc., and a value of 0 when nothing happened on the specified date. This data was manually retrieved by traversing through the nation calendar of the UAE.  
It was found that the attributes Confirmed Cases, Recovered Cases, and Confirmed Deaths had a Minimum of 0. The zero value recorded as the number of cases is irrelevant as it was recorded in the beginning of February in 2020, when there were only 1 or 2 cases per day and the virus was still only contained within China. It was also noticed that the attributes related to the vaccinations had some missing values in the beginning of the pandemic because there was no vaccination present at that time. Thus, these missing values were replaced with 0.  
Figure 4 shows a graphical representation of the time series dataset used in this paper. The graphs are generated for the period from the 1 of February 2020 to the 29 of April 2022. From these graphs we can see that the Recoveries and Confirmed Cases are almost similar in magnitude but vary in the number of peaks and falls. The similarity in magnitude is expected as within the UAE, the recovery rate is extremely higher than the death rate, implying that majority of the people have recovered. We can see in the initial stages of the virus, although the number of Confirmed Cases was low, the number of Recovered Cases was high. This can signify that the Recovered Cases were most likely the initial cases where the effect of the virus was stronger on people due to lack of knowledge and precautionary measures.  
Additionally, we can see that the number of tests conducted are gradually increasing except for the sudden spike in between of October 2021 and January 2022. This can be accounted for by the introduction of the [PERSON] green-pass mobile application, which required people to take a PCR test every 14 days to maintain their green status. There is also an interesting phenomenon where the number of vaccine doses were initially very high but then started to downfall and have swindled down to zero doses given per day. This  
Figure 4: Graphical representation of the time series. The outbreak in the UAE started in February 2020 as can be seen in Figures (**a**–**e**), while in (**f**), the vaccination stated in December 2020.  
can be seen as the UAE has a very high vaccination rate where over 99% of the population are already fully vaccinated [38].