HIV with contact tracing: a case study in approximate Bayesian computation
Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian computation (ABC), an alternative to data imputation methods such as Markov chain Monte Carlo (MCMC) integration, is proposed for making inference in epidemiological models....
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Published in | Biostatistics (Oxford, England) Vol. 11; no. 4; pp. 644 - 660 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
England
Oxford Publishing Limited (England)
01.10.2010
Oxford University Press (OUP) |
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Abstract | Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian computation (ABC), an alternative to data imputation methods such as Markov chain Monte Carlo (MCMC) 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%. |
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AbstractList | Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian computation (ABC), an alternative to data imputation methods such as Markov chain Monte Carlo (MCMC) 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%. [PUBLICATION ABSTRACT] Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian computation (ABC), an alternative to data imputation methods such as Markov chain Monte Carlo (MCMC) 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%. 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 $40\%$. |
Author | Blum, Michael G B Tran, Viet Chi |
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Cites_doi | 10.1016/j.epidem.2008.09.001 10.1137/0319051 10.1214/aos/1176325750 10.1515/JIIP.2007.002 10.1016/j.physa.2009.09.053 10.1093/biostatistics/3.4.493 10.1186/1471-2334-7-130 10.1093/genetics/162.4.2025 10.1016/S0025-5564(02)00109-8 10.1093/biomet/asp052 10.1007/s11222-009-9116-0 10.1111/1467-985X.00125 10.1191/1471082X04st065oa 10.1002/sim.1912 10.1073/pnas.0607208104 10.1098/rsif.2008.0172 10.1111/1467-9868.00177 10.1098/rsif.2007.1292 10.1080/01621459.1992.10476255 10.1093/ije/31.3.679 10.1080/17513750801993266 |
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Snippet | Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian computation (ABC), an alternative... Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to... |
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SubjectTerms | Acquired immune deficiency syndrome AIDS Algorithms Applications Bayes Theorem Bayesian analysis Biostatistics Computation Computer Simulation Contact Tracing Cuba - epidemiology Epidemiology HIV HIV Infections - epidemiology HIV Infections - transmission Human immunodeficiency virus Humans Life Sciences Linear Models Markov Chains Models, Biological Monte Carlo Method Monte Carlo simulation Nonlinear Dynamics Santé publique et épidémiologie Statistics Stochastic Processes |
Title | HIV with contact tracing: a case study in approximate Bayesian computation |
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