About: Inference in Stochastic Epidemic Models via Multinomial Approximations

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Inference in Stochastic Epidemic Models via Multinomial Approximations
Creator	Rimella, Lorenzo Whiteley, Nick
source	ArXiv
abstract	We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability. The method is applicable to a class of discrete-time, finite-population compartmental models with partial, randomly under-reported or missing count observations. In contrast to state-of-the-art alternatives such as Approximate Bayesian Computation techniques, no forward simulation of the model is required and there are no tuning parameters. Evaluating the approximate marginal likelihood of model parameters is achieved through a computationally simple filtering recursion. The accuracy of the approximation is demonstrated through analysis of real and simulated data using a model of the 1995 Ebola outbreak in the Democratic Republic of Congo. We show how the method can be embedded within a Sequential Monte Carlo approach to estimating the time-varying reproduction number of COVID-19 in Wuhan, China, recently published by Kucharski et al. 2020.
has issue date	2020-06-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	4a526705cb89d405db335b0a153ec7f7f0097336
resource representing a document's title	Inference in Stochastic Epidemic Models via Multinomial Approximations
resource representing a document's body	covid:4a526705cb89d405db335b0a153ec7f7f0097336#body_text
is schema:about of	named entity 'randomly' named entity 'Approximate Bayesian Computation' named entity 'COVID-19' named entity 'model' named entity 'Inference' »more»

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