  Coronavirus disease (COVID-19) spread forecasting is an important task to
track the growth of the pandemic. Existing predictions are merely based on
qualitative analyses and mathematical modeling. The use of available big data
with machine learning is still limited in COVID-19 growth prediction even
though the availability of data is abundance. To make use of big data in the
prediction using deep learning, we use long short-term memory (LSTM) method to
learn the correlation of COVID-19 growth over time. The structure of an LSTM
layer is searched heuristically until the best validation score is achieved.
First, we trained training data containing confirmed cases from around the
globe. We achieved favorable performance compared with that of the recurrent
neural network (RNN) method with a comparable low validation error. The
evaluation is conducted based on graph visualization and root mean squared
error (RMSE). We found that it is not easy to achieve the same quantity of
confirmed cases over time. However, LSTM provide a similar pattern between the
actual cases and prediction. In the future, our proposed prediction can be used
for anticipating forthcoming pandemics. The code is provided here:
https://github.com/cbasemaster/lstmcorona
