Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19. (arXiv:2101.05304v1 [cs.LG])

In this work we present a spatial-temporal convolutional neural network for
predicting future COVID-19 related symptoms severity among a population, per
region, given its past reported symptoms. This can help approximate the number
of future Covid-19 patients in each region, thus enabling a faster response,
e.g., preparing the local hospital or declaring a local lockdown where
necessary. Our model is based on a national symptom survey distributed in
Israel and can predict symptoms severity for different regions daily. The model
includes two main parts – (1) learned region-based survey responders profiles
used for aggregating questionnaires data into features (2) Spatial-Temporal 3D
convolutional neural network which uses the above features to predict symptoms
progression.