About: Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams

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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	Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
Creator	Chowell, Gerardo Castillo-Chavez, Carlos Bettencourt, Luís, M Ribeiro, Ruy Lant, Timothy
Source	PMC
abstract	An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.
has issue date	2007-01-01(xsd:dateTime)
bibo:doi	10.1007/978-3-540-72608-1_8
has license	no-cc
sha1sum (hex)	1ccd924dbf169e600355923da3de6e6a8ac217c1
schema:url	https://doi.org/10.1007/978-3-540-72608-1_8
resource representing a document's title	Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
has PubMed Central identifier	PMC7122958
schema:publication	Intelligence and Security Informatics: Biosurveillance
resource representing a document's body	covid:1ccd924dbf169e600355923da3de6e6a8ac217c1#body_text
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