## 2 Material and Methods
### Design Methodology  
In this paper, three DL algorithms are used for predicting the one-day-ahead number of new infections based on the daily new infections of the previous 14 days. We used the previous 14 days as input to consider the influence of all possible virus carriers when transmitting the disease. According to the WHO, the transmission of the virus from people with infections to others continues from the Day 2 to day 10 or 13 for symptomatic or asymptomatic individuals, respectively. Thus, all people carrying the virus even 13 days before contribute to the next-day infections. Moreover, as per the \"Criteria for releasing COVID-19 patients from isolation Geneva: World Health Organization; 2020 (availableat [[https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation](https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation)]([https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation](https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation)) (accessed on 30 July 2022)), any person with an infection should be released within 14 days, which is likely the period during which they are able to transmit the virus to others. However, the number of days that may contribute to the coming days' infections depends on the COVID-19 variant, the location, and the period. As an example, the authors in [23] chose the length of the subsequence as being equal to 20 consecutive days. In addition, according to a study conducted in Japan for the BA.1 omicron variant, the incubation period was found to vary from 1.3 days to 9.6 days [32]. Taking into account the fact that DL techniques are robust against uncertainties, the choice of the previous 14 days is likely to be reasonable. Regarding the model itself, the structures of the deep learning (DL) algorithms used in this study are highly nonlinear and involve interactions between the numbers of infections of the previous days to model the number of infections in the forthcoming days. Accordingly, the DL includes several multiplicative terms, which indicate the highly nonlinear character of the models adopted. The problem of modeling SARS-CoV-2 transmission dynamics is considered difficult because it depends on many factors (the health system efficiency, vaccine availability, climatic conditions, demography of the location, the movement of people, the day of the week [33], etc.) including also \"the measures taken by the government\" to mitigate/limit the spread of the disease. In the proposed DL, all of those factors, in addition to any other hidden factor, are included implicitly in the number of daily new infections. As per the DL structure, those factors are retained during training via the memory cells. The forecasting procedure is depicted in Figure 1. The first step consists of the COVID-19 dataset collection. The collected dataset is then divided into two subsets: 80% used for the models' training (in-sample), and the remaining 20% employed for the models' validation and testing (out-of-sample). Three performance metrics, namely, the mean absolute percentage error (MAPE), the coefficient of determination (\(R^{2}\)), and the root mean square error (RMSE), are calculated to measure the accuracy of the investigated models. More details about the operation of the three DL algorithms as well as the performance metrics' calculation will be provided in the following subsections.  
Figure 1: Flowchart of the prediction procedure based on three different DL architectures.