HIV with contact-tracing: a case study in ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
HIV with contact-tracing: a case study in Approximate Bayesian Computation
Auteur(s) :
Blum, Michael G B [Auteur correspondant]
Tran, Chi [Auteur correspondant]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Tran, Chi [Auteur correspondant]
![refId](/themes/Mirage2//images/idref.png)
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Titre de la revue :
Biostatistics
Pagination :
644-660
Éditeur :
Oxford University Press (OUP)
Date de publication :
2010-10
ISSN :
1465-4644
Mot(s)-clé(s) en anglais :
Mathematical epidemiology
stochastic SIR model
unobserved infectious population
simulation-based inference
likelihood-free inference
stochastic SIR model
unobserved infectious population
simulation-based inference
likelihood-free inference
Discipline(s) HAL :
Statistiques [stat]/Applications [stat.AP]
Sciences du Vivant [q-bio]/Santé publique et épidémiologie
Statistiques [stat]/Calcul [stat.CO]
Sciences du Vivant [q-bio]/Santé publique et épidémiologie
Statistiques [stat]/Calcul [stat.CO]
Résumé en anglais : [en]
Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, ...
Lire la suite >Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed for making inference in epidemiological models. It is a likelihood-free method that relies exclusively on numerical simulations. ABC consists in computing a distance between simulated and observed summary statistics and weighting the simulations according to this distance. We propose an original extension of ABC to path-valued summary statistics, corresponding to the cumulated number of detections as a function of time. For a standard compartmental model with Suceptible, Infectious and Recovered individuals (SIR), we show that the posterior distributions obtained with ABC and MCMC are similar. In a refined SIR model well-suited to the HIV contact-tracing data in Cuba, we perform a comparison between ABC with full and binned detection times. For the Cuban data, we evaluate the efficiency of the detection system and predict the evolution of the HIV-AIDS disease. In particular, the percentage of undetected infectious individuals is found to be of the order of $40\%$.Lire moins >
Lire la suite >Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed for making inference in epidemiological models. It is a likelihood-free method that relies exclusively on numerical simulations. ABC consists in computing a distance between simulated and observed summary statistics and weighting the simulations according to this distance. We propose an original extension of ABC to path-valued summary statistics, corresponding to the cumulated number of detections as a function of time. For a standard compartmental model with Suceptible, Infectious and Recovered individuals (SIR), we show that the posterior distributions obtained with ABC and MCMC are similar. In a refined SIR model well-suited to the HIV contact-tracing data in Cuba, we perform a comparison between ABC with full and binned detection times. For the Cuban data, we evaluate the efficiency of the detection system and predict the evolution of the HIV-AIDS disease. In particular, the percentage of undetected infectious individuals is found to be of the order of $40\%$.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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