Abstract
Covid-19 has exerted tremendous pressure on countries’ resources, especially the health sector. Thus, it was important for governments to predict the number of new covid-19 cases to face this sudden epidemic. Deep learning techniques have shown success in predicting new covid-19 cases. Researchers have used long-short term memory (LSTM) networks that consider the previous covid-19 numbers to predict new ones. In this work, we use LSTM networks to predict new covid-19 cases in Jordan and the United Arab Emirates (UAE) for six months. The populations of both countries are almost the same; however, they had different arrangements to deal with the epidemic. The UAE was a world leader in terms of the number of covid-19 tests per capita. Thus, we try to find if incorporating covid-19 tests in predicting the LSTM networks would improve the prediction accuracy. Building bi-variate LSTM models that consider the number of tests did not improve uni-variate LSTM models that only consider previous covid-19 cases. However, using a uni-variate LSTM model to predict the ratio of covid-19 cases to the number of covid-19 tests have shown superior results in the case of Jordan. This ratio can be used to forecast the number of new covid-19 cases by multiplying this ratio by the number of conducted tests.
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Al-Shihabi, S., Abu-Abdoun, D.I. (2022). What to Forecast When Forecasting New Covid-19 Cases? Jordan and the United Arab Emirates as Case Studies. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-92666-3_31
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