About: Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.
Creator	Zhang, Lu Rimoin, Anne Suchard, Marc Ramirez, Christina Bufford, Teresa Shamshoian, John Sundin, Phillip Watson, Gregory Xiong, Di Zoller, Joseph
Source	MedRxiv
abstract	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 Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.
has issue date	2020-05-18(xsd:dateTime)
bibo:doi	10.1101/2020.05.15.20102608
has license	medrxiv
sha1sum (hex)	09dcaabd15cd76c043b07aaf73f581cd01503a38
schema:url	https://doi.org/10.1101/2020.05.15.20102608
resource representing a document's title	Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.
resource representing a document's body	covid:09dcaabd15cd76c043b07aaf73f581cd01503a38#body_text
is schema:about of	named entity 'curve' named entity 'COVID-19' named entity 'individuals' named entity 'empirically' named entity 'sophistication' named entity 'pandemic' named entity 'U.S.' named entity 'virus' named entity 'interval' named entity 'dynamic' named entity 'COVID-19' named entity 'LEVEL' named entity 'ABSOLUTE' named entity 'VELOCITY' named entity 'MORTALITY' named entity 'RANDOM FOREST' named entity 'SHIFTING' named entity 'VIRUS' named entity 'PROVIDE' named entity 'DEATHS' named entity 'mortality' named entity 'critical' named entity 'derivative' named entity 'data' named entity 'illustrated' named entity 'trajectory' named entity 'locations' named entity 'mixed model' named entity 'velocity' named entity 'case' named entity 'COVID-19' named entity 'Mississippi' named entity 'velocity' named entity 'random forest' named entity 'algorithm' named entity 'forecasting accuracy' named entity 'random forest' named entity 'information to obtain' named entity 'random forest' named entity 'Bayesian' named entity 'uncertainty' named entity 'MASE' named entity 'ARIMA' named entity 'Gaussian noise' named entity 'data collected' named entity 'agent-based' named entity 'transition rates' named entity 'mixed model' named entity 'COVID-19 pandemic' named entity 'New York' named entity 'MSE' named entity 'velocity' named entity 'permutation' named entity 'differential equations' named entity 'deterministic' named entity 'mixed model' named entity 'observed data' named entity 'COVID' named entity 'velocity' named entity 'upper bound' named entity 'epidemic' named entity 'velocity' named entity 'New York' named entity 'ODEs' named entity 'random forest' named entity 'velocity' named entity 'Ohio' named entity 'learning algorithms' named entity 'SARS-CoV-2 virus' named entity 'sophisticated approach' named entity 'influenza pandemic' named entity 'infection' named entity 'vector' named entity 'medRxiv' named entity 'velocity' named entity 'random effects'

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