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

EMNLP - October 31, 2021

Evaluation Paradigms in Question Answering

Pedro Rodriguez, Jordan Boyd-Graber

ASRU - December 13, 2021

Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems

Yangyang Xia, Buye Xu, Anurag Kumar

Uncertainty and Robustness in Deep Learning Workshop at ICML - June 24, 2021

DAIR: Data Augmented Invariant Regularization

Tianjian Huang, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

IROS - September 1, 2021

Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

Naoki Yokoyama, Sehoon Ha, Dhruv Batra

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