Title :

HIV with contact-tracing: a case study in Approximate Bayesian Computation

Author(s) :

Blum, Michael G B [Auteur correspondant]
Tran, Chi [Auteur correspondant]
Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Journal title :

Biostatistics

Pages :

644-660

Publisher :

Oxford University Press (OUP)

Publication date :

2010-10

ISSN :

1465-4644

English keyword(s) :

Mathematical epidemiology
stochastic SIR model
unobserved infectious population
simulation-based inference
likelihood-free inference

HAL domain(s) :

Statistiques [stat]/Applications [stat.AP]
Sciences du Vivant [q-bio]/Santé publique et épidémiologie
Statistiques [stat]/Calcul [stat.CO]

English abstract : [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, ...
Show more >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 $40\%$.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Modèles Aléatoires eN Ecologie, Génétique et Evolution

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL