ABCtoolbox: a versatile toolkit for approximate Bayesian computations

BACKGROUND: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. RESULTS: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. CONCLUSION: ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results

Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling Approach

This article describes the use of an original modeling approach to assess the risk of yellow fever (YF) epidemics. YF is a viral hemorrhagic fever responsible in past centuries for devastating outbreaks. Since the 1930s, a vaccine has been available that protects the individual for at least 10 years, if not for life. However, immunization of populations in African countries was gradually discontinued after the 1960s. With the decrease in immunity against YF in African populations the disease reemerged in the 1980s. In 2005, WHO, UNICEF, and the GAVI Alliance decided to support preventive vaccination of at-risk populations in West African endemic countries in order to tackle the reemergence of YF and reduce the risk of urban YF outbreaks. Financial resources were made available to scale up a global YF vaccine stockpile and to support countries with limited resources in the management of preventive vaccination campaigns. This article describes the process we used to determine the most at-risk populations using a mathematical model to prioritize targeted immunization campaigns. We believe that this approach could be useful for other diseases for which decision making process is difficult because of limited data availability, complex risk variables, and a need for rapid decisions and implementation