Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a B...

Full description

Saved in:
Bibliographic Details
Main Authors: Gregory L Watson, Di Xiong, Lu Zhang, Joseph A Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W Rimoin, Marc A Suchard, Christina M Ramirez
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-03-01
Series:PLoS Computational Biology
Online Access:https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008837&type=printable
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items