  The global pandemic caused by COVID-19 affects our lives in all aspects. As
of September 11, more than 28 million people have tested positive for COVID-19
infection, and more than 911,000 people have lost their lives in this virus
battle. Some patients can not receive appropriate medical treatment due the
limits of hospitalization volume and shortage of ICU beds. An estimated future
hospitalization is critical so that medical resources can be allocated as
needed. In this study, we propose to use 4 recurrent neural networks to infer
hospitalization change for the following week compared with the current week.
Results show that sequence to sequence model with attention achieves a high
accuracy of 0.938 and AUC of 0.850 in the hospitalization prediction. Our work
has the potential to predict the hospitalization need and send a warning to
medical providers and other stakeholders when a re-surge initializes.
