About: AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning

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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
title	AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning
Creator	Chawla, Sanjay Rizzo, Stefano Saad, Mohamad Vantini, Giovanna
source	MedRxiv
abstract	Since the SARS-CoV-2 virus outbreak has been recognized as a pandemic on March 11, 2020, several models have been proposed to forecast its evolution following the governments' interventions. In particular, the need for fine-grained predictions, based on real-time and fluctuating data, has highlighted the limitations of traditional SEIR models and parameter fitting, encouraging the study of new models for greater accuracy. In this paper we propose a novel approach to epidemiological parameter fitting and epidemic forecasting, based on an extended version of the SEIR compartmental model and on an auto-differentiation technique for partially observable ODEs (Ordinary Differential Equations). The results on publicly available data show that the proposed model is able to fit the daily cases curve with greater accuracy, obtaining also a lower forecast error. Furthermore, the forecast accuracy allows to predict the peak with an error margin of less than one week, up to 50 days before the peak happens.
has issue date	2020-07-28(xsd:dateTime)
bibo:doi	10.1101/2020.07.25.20159715
has license	medrxiv
sha1sum (hex)	58f0d2f01b9cf258816f78fd46fae940b3c8489b
schema:url	https://doi.org/10.1101/2020.07.25.20159715
resource representing a document's title	AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning
resource representing a document's body	covid:58f0d2f01b9cf258816f78fd46fae940b3c8489b#body_text
is schema:about of	named entity 'limitations' named entity 'models' named entity 'fit' named entity 'evolution' named entity 'greater' »more»

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