# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
###### Abstract  
Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities' preparedness for future pandemics. COVID-19; UAE; machine learning; deep learning; forecasting; trend analysis +
Footnote †: journal: MedicalA pandemic of this nature tends to vary in magnitude over time due to the inherent nature of the virus that is constantly adapting, in addition to the non-negligible effects of seasonal changes and various environmental factors. This general phenomenon is exemplified in the case of the evolution of the initial strain of the novel Coronavirus into the rapidly spreading and arguably more harmful Delta or Omicron variants [4]. As a consequence of the multiple aspects influencing the contagion susceptibility of the Coronavirus and its variant, the relationship between the contributing components and the virus itself tend to be non-linear [5]. This necessitates the utilization of machine learning/deep learning algorithms that can capture these underlying relationships effectively. Statistical techniques make generalized assumptions about the intrinsic behavior of the data (i.e., the daily instances of COVID-19 cases, vaccinations, etc.). These assumptions pertain, more specifically, to the measures of central tendency associated with the periodic, fixed interval measurements of the data samples of interest. For instance, linear correlations in this historical data appear to be incapable of detecting non-spurious patterns of COVID-19-specific factors due to its aforementioned non-linear and potentially complex nature [6]. Machine learning has been proven to work effectively with chaotic, noisy data even without a priori domain knowledge (although, this can impact its performance favorably if available) and hence can be a solution for recognizing patterns of interest in a continuum of equally spaced data measurements such as the daily case counts of COVID-19. To learn more about this phenomenon, this paper focuses on the United Arab Emirates (UAE) as a case study.