Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

AbstractOur ability to accurately infer transmission patterns of infectious diseases is critical to monitor both their spread and the efficacy of public health policies. The use of phylogenetic methods for the reconstruction of viral ancestral relationships has garnered increasing interest, particularly in the characterization of HIV epidemics and sub-epidemics. In the case of this virus, the Swiss HIV Cohort Study (SHCS) contains a wide breadth of genomic data that have been widely used as a means of applying such methods. However, current approaches for quantifying the epidemiological dynamics of diseases are computationally intensive, and fail to scale well with this magnitude of data. To address this issue, we re-implement an Approximate Bayesian Computation (ABC) approach based on sequential Monte Carlo (SMC). By means of simulations, we demonstrate that our implementation is capable of inferring key epidemiological parameters of the Swiss HIV epidemic accurately, and that sampling intensity has no significant effect on the accuracy of our estimates. Applied to a subset of HIV sequences from the SHCS, we show that we can distinguish sub-epidemics that are circulating in culturally distinct Swiss regions. Given these findings, we propose that ABC-SMC samplers will allow us to evaluate the impact of new public health policies, such as the implementation of a needle exchange program in the case of HIV, based on genetic data sampled before and after the implementation of a new policy.

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Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling

SIAM/ASA Journal on Uncertainty Quantification ◽

10.1137/20m1316160 ◽

2021 ◽

Vol 9 (2) ◽

pp. 788-817

Author(s):

Thomas P. Prescott ◽

Ruth E. Baker

Keyword(s):

Monte Carlo ◽

Approximate Bayesian Computation ◽

Sequential Monte Carlo ◽

Bayesian Computation ◽

Approximate Bayesian

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Approximate Bayesian Computation Algorithms for Estimating Network Model Parameters

10.1101/106450 ◽

2017 ◽

Author(s):

Ye Zheng ◽

Stéphane Aris-Brosou

Keyword(s):

Monte Carlo ◽

Approximate Bayesian Computation ◽

Sequential Monte Carlo ◽

Likelihood Function ◽

Model Parameters ◽

Human Populations ◽

Bayesian Computation ◽

Transmission Networks ◽

General Network ◽

Approximate Bayesian

AbstractStudies on Approximate Bayesian Computation (ABC) replacing the intractable likelihood function in evaluation of the posterior distribution have been developed for several years. However, their field of application has to date essentially been limited to inference in population genetics. Here, we propose to extend this approach to estimating the structure of transmission networks of viruses in human populations. In particular, we are interested in estimating the transmission parameters under four very general network structures: random, Watts-Strogatz, Barabasi-Albert and an extension that incorporates aging. Estimation was evaluated under three approaches, based on ABC, ABC-Markov chain Monte Carlo (ABC-MCMC) and ABC-Sequential Monte Carlo (ABC-SMC) samplers. We show that ABC-SMC samplers outperform both ABC and ABC-MCMC, achieving high accuracy and low variance in simulations. This approach paves the way to estimating parameters of real transmission networks of transmissible diseases.

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