# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 6 Conclusions
### Limitations and Future Work  
Overall, we see that the simple univariate LSTM performs the best, with some additional information learned through the new added event related variables. We can also see that some variations of the bidirectional LSTM are very promising. The major limitation to this work is the nature of the data. The data are purely numerical and statistical and lack depth in regard to the contextual severity of the effects of COVID-19 in the UAE. Another limitation is that the data are represented as a whole for the UAE and are not divided into districts. By dividing the data into districts, the model could be more accurate, as Emirates such as Abu Dhabi had more restrictions which would have slowed the spread there, whereas Emirates such as Shariah that have a higher population would have caused an increase in spread. As these data were not provided by the government, studying the spread of COVID-19 by city of district was not addressed in this work; however, this observation can be taken into consideration during data reporting for future pandemics. There is an additional limitation in regard to the Vaccination attribute, as it is just a number representing the number of doses administered per day and it does not consider which dose it is or whether a person who was vaccinated contracted COVID or not. Similarly, there is a challenge in regard to the number of cases, as we do not have any information on whether all are new cases or if a person has contracted the virus repeatedly. For future work, further variable selection and analysis can be applied, especially on the weakly trained LSTM-AE, to see if the performance of the model increases or decreases by using sets of meaningful and uncorrelated regressors. Another major challenge is the difficulty of manually creating and updating the event variables. The information is difficult to find online and this is a very tedious job. There is also room for human error. A solution for this would be to follow [21] and build and train a Natural Language Processing (NLP) model that can scrap English and Arabic news sites and tweets to record important information related to the happening of a particular day. Another idea to try in the future is inspired by a recent survey published by authors [PERSON] et al. [45], who concluded that a simple singular model might not be efficient for predicting the trends in COVID-19 and that we might have to venture into hybrid and ensemble models to play with the advantages of various different models and accurately learn the seasonality of the trends. However, we consider this work to be of some importance to the research community, as it quantitatively shows the performance of the base LSTM model for modelling a novel and rapidly mutating contagion like the COVID-19 virus within the United Arab Emirates. Conceptualization, S.D., M.P. and A.S.; methodology, D.S. ; software, D.S. ; validation, D.S. ; formal analysis, D.S., S.D., M.P. and A.S.; investigation, D.S. ; resources, D.S. ; data curation, D.S. ; writing--original draft preparation, D.S. ; writing--review and editing, S.D., M.P. and A.S.; visualization, D.S. ; supervision, S.D., M.P. and A.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The dataset used in this work is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) @: [[https://fcc.gov.ae/en-us](https://fcc.gov.ae/en-us)]([https://fcc.gov.ae/en-us](https://fcc.gov.ae/en-us)) (accessed on 30 April 2024). Acknowledgments: The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah (Award #: OAPCEN-1410-E00282). This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.