Publication

Neural Relational Autoregression for High-Resolution COVID-19 Forecasting

arXiv


Abstract

Forecasting COVID-19 poses unique challenges due to the novelty of the disease, its unknown characteristics, and substantial but varying interventions to reduce its spread. To improve the quality and robustness of forecasts, we propose a new method which aims to disentangle region-specific factors – such as demographics, enacted policies, and mobility – from disease-inherent factors that influence its spread. For this purpose, we combine recurrent neural networks with a vector autoregressive model and train the joint model with a specific regularization scheme that increases the coupling between regions. This approach is akin to using Granger causality as a relational inductive bias and allows us to train high-resolution models by borrowing statistical strength across regions. In our experiments, we observe that our method achieves strong performance in predicting the spread of COVID-19 when compared to state-of-the-art forecasts.

Related Publications

All Publications

CVPR - June 18, 2021

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi

CVPR - June 18, 2021

Discovering Relationships between Object Categories via Universal Canonical Maps

Natalia Neverova, Artsiom Sanakoyeu, Patrick Labatut, David Novotny, Andrea Vedaldi

CVPR - June 17, 2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

Zihang Meng, Licheng Yu, Ning Zhang, Tamara Berg, Babak Damavandi, Vikas Singh, Amy Bearman

DSN - June 21, 2021

Near-Realtime Server Reboot Monitoring and Root Cause Analysis in a Large-Scale System

Fred Lin, Bhargav Bolla, Eric Pinkham, Neil Kodner, Daniel Moore, Amol Desai, Sriram Sankar

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy