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.
@article{arxiv.2101.05304,
title = {Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19},
author = {Ravid Shwartz-Ziv and Itamar Ben Ari and Amitai Armon},
journal= {arXiv preprint arXiv:2101.05304},
year = {2021}
}