About: Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model
Creator	Marathe, Madhav Venkatramanan, Srinivasan Ben, Xue O'banion, Shawn Sadilek, Adam Eastham, Paul Green, Bradley Rough, Kathryn Wu, Neo
Source	MedRxiv
abstract	Timely interventions and early preparedness of healthcare resources are crucial measures to tackle the mbox{COVID-19} disease. To aid these efforts, we developed the Mobility-Augmented SEIR model (mbox{MA-SEIR}) that leverages Google's aggregate and anonymized mobility data to augment classic compartmental models. We show in a retrospective analysis how this method can be applied at an early stage in the mbox{COVID-19} epidemic to forecast its subsequent spread and onset in different geographic regions, with minimal parameterization of the model. This provides insight into the role of near real-time aggregate mobility data in disease spread modeling by quantifying substantial changes in how populations move both locally and globally. These changes would be otherwise very hard to capture using less timely data.
has issue date	2020-07-29(xsd:dateTime)
bibo:doi	10.1101/2020.07.27.20159996
has license	medrxiv
sha1sum (hex)	132ecfd6698fdd67ebfd4c96e98e9b19309df83a
schema:url	https://doi.org/10.1101/2020.07.27.20159996
resource representing a document's title	Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model
resource representing a document's body	covid:132ecfd6698fdd67ebfd4c96e98e9b19309df83a#body_text
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