About: A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

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About: A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models Goto Sponge NotDistinct Permalink

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	A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
Creator	Santillana, Mauricio Vespignani, Alessandro Davis, Jessica Chinazzi, Matteo Poirier, Canelle Clemente, Leonardo Ding, Xiyu Liu, Dianbo
Source	ArXiv
abstract	We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.
has issue date	2020-04-08(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d3061a3bc3718d7397965b088870577b341487e9
resource representing a document's title	A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
resource representing a document's body	covid:d3061a3bc3718d7397965b088870577b341487e9#body_text
is schema:about of	named entity 'forecasts' named entity 'outbreaks' named entity 'agent-based' named entity 'forecasting' named entity 'OBSERVATIONS' named entity 'DECISION' named entity 'EXTENDED' named entity 'OUTBREAKS' named entity 'SEARCH' named entity 'RELATED' named entity '28A' named entity 'REAL-TIME' named entity 'ESTIMATES' named entity 'CURRENTLY' named entity 'BASELINE' named entity 'CONTROL' named entity 'HISTORICAL' named entity 'MAKERS' named entity 'STABLE' named entity 'NEWS MEDIA' named entity 'METHODOLOGIES' named entity 'NUMBER OF' named entity 'FORECAST' named entity 'TIMELY' named entity 'Apr' named entity 'produce' named entity 'COVID-19' named entity 'mechanistic' named entity 'synchronicities' named entity 'Media Cloud' named entity 'Baidu' named entity 'COVID-19' named entity 'Liaoning' named entity 'cough' named entity 'China' named entity 'Baidu' named entity 'epidemiological data' named entity 'COVID' named entity 'Internet' named entity 'Chinese provinces' named entity 'autoregressive model' named entity 'posterior distribution' named entity 'COVID-19' named entity 'Liaoning' named entity 'time evolution' named entity 'data augmentation' named entity 'emerging disease' named entity 'Internet' named entity 'Qinghai' named entity 'Hong Kong' named entity 'COVID' named entity 'similarity matrix' named entity 'Heilongjiang' named entity 'pneumonia' named entity 'Mandarin' named entity 'emerging disease' named entity 'COVID' named entity 'Public Health Emergency of International Concern' named entity 'COVID' named entity 'Epidemic' named entity 'data quality' named entity 'Wuhan' named entity 'Internet' named entity 'Baidu' named entity 'L1 norm' named entity 'correlation matrix' named entity 'CDC' named entity 'COVID' named entity 'data augmentation' named entity 'standard deviation' named entity 'Macau' named entity 'root mean square error' named entity 'COVID-19' named entity 'Chinese provinces' named entity 'Wuhan' named entity 'coronavirus' named entity 'World Health Organization' named entity 'Taiwan' named entity 'COVID-19' named entity 'Chinese provinces'

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