scholarly journals Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Waleed Almutiry ◽  
Rob Deardon
Keyword(s):  
Infectious Disease ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Approximate Bayesian Computation ◽  
Disease Model ◽  
Simulated Data ◽  
Computation Time ◽  
Contact Network ◽  
Bayesian Computation ◽  
Approximate Bayesian

AbstractInfectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

scholarly journals Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach

2018 ◽  
Vol 17 ◽  
pp. 117693511878692
Author(s):  
Kashyap Nagaraja ◽  
Ulisses Braga-Neto
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sample Size ◽  
Approximate Bayesian Computation ◽  
Selected Reaction Monitoring ◽  
Bayesian Computation ◽  
Reaction Monitoring ◽  
Approximate Bayesian

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.

scholarly journals A Revised Model of Anatomically Modern Human Expansions Out of Africa through a Machine Learning Approximate Bayesian Computation Approach

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1510
Author(s):  
Maria Teresa Vizzari ◽  
Andrea Benazzo ◽  
Guido Barbujani ◽  
Silvia Ghirotto
Keyword(s):  
Approximate Bayesian Computation ◽  
Simulated Data ◽  
Modern Human ◽  
Genomic Variation ◽  
Population History ◽  
Bayesian Computation ◽  
Dispersal Pattern ◽  
High Coverage ◽  
Out Of Africa ◽  
Approximate Bayesian

There is a wide consensus in considering Africa as the birthplace of anatomically modern humans (AMH), but the dispersal pattern and the main routes followed by our ancestors to colonize the world are still matters of debate. It is still an open question whether AMH left Africa through a single process, dispersing almost simultaneously over Asia and Europe, or in two main waves, first through the Arab Peninsula into southern Asia and Australo-Melanesia, and later through a northern route crossing the Levant. The development of new methodologies for inferring population history and the availability of worldwide high-coverage whole-genome sequences did not resolve this debate. In this work, we test the two main out-of-Africa hypotheses through an Approximate Bayesian Computation approach, based on the Random-Forest algorithm. We evaluated the ability of the method to discriminate between the alternative models of AMH out-of-Africa, using simulated data. Once assessed that the models are distinguishable, we compared simulated data with real genomic variation, from modern and archaic populations. This analysis showed that a model of multiple dispersals is four-fold as likely as the alternative single-dispersal model. According to our estimates, the two dispersal processes may be placed, respectively, around 74,000 and around 46,000 years ago.

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