About: Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
Creator	Chiang, Wen-Hao Liu, Xueying Mohler, George
source	MedRxiv
abstract	Hawkes processes are used in machine learning for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on short-term forecasting tasks, showing that the Hawkes process outperforms several benchmark models currently used to track the pandemic, including an ensemble approach and an SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.
has issue date	2020-06-08(xsd:dateTime)
bibo:doi	10.1101/2020.06.06.20124149
has license	medrxiv
sha1sum (hex)	5c49fdcbfa20cc92d8f375fc0a52df5110b4a0b1
schema:url	https://doi.org/10.1101/2020.06.06.20124149
resource representing a document's title	Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
resource representing a document's body	covid:5c49fdcbfa20cc92d8f375fc0a52df5110b4a0b1#body_text
is schema:about of	named entity 'compartmental' named entity 'Google' named entity 'clustering' named entity 'COVID-19' named entity 'SHORT-TERM' »more»

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