  The outbreak of COVID-19 i.e. a variation of coronavirus, also known as novel
corona virus causing respiratory disease is a big concern worldwide since the
end of December 2019. As of September 12, 2020, it has turned into an epidemic
outbreak with more than 29 million confirmed cases and around 1 million
reported deaths worldwide. It has created an urgent need to monitor and
forecast COVID-19 spread behavior to better control this spread. Among all the
popular models for COVID-19 forecasting, statistical models are receiving much
attention in media. However, statistical models are showing less accuracy for
long term forecasting, as there is high level of uncertainty and required data
is also not sufficiently available. In this paper, we propose a comparative
analysis of deep learning models to forecast the COVID-19 outbreak as an
alternative to statistical models. We propose a new Attention-based
encoder-decoder model, named Attention-Long Short Term Memory (AttentionLSTM).
LSTM based neural network layer architecture incorporates the idea of
fine-grained attention mechanism i.e., attention on hidden state dimensions
instead of hidden state vector itself, which is capable of highlighting the
importance and contribution of each hidden state dimension. It helps in
detection on crucial temporal information, resulting in a highly interpretable
network. Additionally, we implement a learnable vector embedding for time. As,
time in a vector representation can be easily added with many architectures.
This vector representation is called Time2Vec. We have used COVID-19 data
repository by the Center for Systems Science and Engineering (CSSE) at Johns
Hopkins University to assess the proposed model's performance. The proposed
model give superior forecasting accuracy compared to other existing methods.
