  We have entered an era of a pandemic that has shaken the world with major
impact to medical systems, economics and agriculture. Prominent computational
and mathematical models have been unreliable due to the complexity of the
spread of infections. Moreover, lack of data collection and reporting makes any
such modelling attempts unreliable. Hence we need to re-look at the situation
with the latest data sources and most comprehensive forecasting models. Deep
learning models such as recurrent neural networks are well suited for modelling
temporal sequences. In this paper, prominent recurrent neural networks, in
particular \textit{long short term memory} (LSTMs) networks, bidirectional
LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting
the spread of COVID-infections among selected states in India. We select states
with COVID-19 hotpots in terms of the rate of infections and compare with
states where infections have been contained or reached their peak and provide
two months ahead forecast that shows that cases will slowly decline. Our
results show that long-term forecasts are promising which motivates the
application of the method in other countries or areas. We note that although we
made some progress in forecasting, the challenges in modelling remain due to
data and difficulty in capturing factors such as population density, travel
logistics, and social aspects such culture and lifestyle.
