An early prediction of covid-19 associated hospitalization surge using deep learning approach
This work addresses the critical need for early hospitalization surge predictions to help medical providers and stakeholders allocate resources effectively during the COVID-19 pandemic, though it is incremental as it applies existing deep learning methods to this specific domain.
The study tackled the problem of predicting COVID-19 hospitalization surges to aid medical resource allocation, achieving a high accuracy of 0.938 and AUC of 0.850 using a sequence-to-sequence model with attention.
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.