  In late 2019, COVID-19, a severe respiratory disease, emerged, and since
then, the world has been facing a deadly pandemic caused by it. This ongoing
pandemic has had a significant effect on different aspects of societies. The
uncertainty around the number of daily cases made it difficult for
decision-makers to control the outbreak. Deep Learning models have proved that
they can come in handy in many real-world problems such as healthcare ones.
However, they require a lot of data to learn the features properly and output
an acceptable solution. Since COVID-19 has been a lately emerged disease, there
was not much data available, especially in the first stage of the pandemic, and
this shortage of data makes it challenging to design an optimized model. To
overcome these problems, we first introduce a new dataset with augmented
features and then forecast COVID-19 cases with a new approach, using an
evolutionary neural architecture search with Binary Bat Algorithm (BBA) to
generate an optimized deep recurrent network. Finally, to show our approach's
effectiveness, we conducted a comparative study on Iran's COVID-19 daily cases.
The results prove our approach's capability to generate an accurate deep
architecture to forecast the pandemic cases, even in the early stages with
limited data.
