Published 24 August 2020)
A new Long short-term memory (LSTM) based artificial recurrent neural network architecture called AICov (published in J. Data Science) was developed as an integrative deep learning framework for COVID-19 forecasting with population covariates. Saumyadipta Pyne and his collaborator Prof. Geoffrey Fox at Indiana University, Bloomington, and coworkers integrated multiple different strategies based on LSTM into AICov to not only include data on the disease but, additionally, socioeconomic covariates and various risk factors at a local level. The compiled data are fed into AICov, leading to a powerful deep learning framework for improved outcome prediction.