About: Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus

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About: Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus 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	Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
Creator	Lu, Y Wu, J Dobronyi, C Gourieroux, C Jasiak, J Monfort, A Rilstone, P
Source	ArXiv
abstract	A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detected individuals and total daily death counts. This model-based approach is illustrated in an application to French data.
has issue date	2020-05-09(xsd:dateTime)
has license	arxiv
sha1sum (hex)	9ea60fa23541771c991f7dee4305795a868bf8d3
resource representing a document's title	Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
resource representing a document's body	covid:9ea60fa23541771c991f7dee4305795a868bf8d3#body_text
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