Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe.

ABSTRACT: Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restr

SUBMITTER: Ibrahim MR 

PROVIDER: S-EPMC7842932 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature