Quasi-Neutral theory of epidemic outbreaks

Some epidemics have been empirically observed to exhibit outbreaks of all possible sizes, i.e., to be scalefree or scale-invariant. Different explanations for this finding have been put forward; among them there is a model for "accidental pathogens" which leads to power-law distributed outbreaks without apparent need of parameter fine tuning. This model has been claimed to be related to self-organized criticality, and its critical properties have been conjectured to be related to directed percolation. Instead, we show that this is a (quasi) neutral model, analogous to those used in Population Genetics and Ecology, with the same critical behavior as the voter-model, i.e. the theory of accidental pathogens is a (quasi)-neutral theory. This analogy allows us to explain all the system phenomenology, including generic scale invariance and the associated scaling exponents, in a parsimonious and simple way.Comment: 13 pages, 6 figures Accepted for publication in PLoS ONE the text have been modified in orden to improve the figure's resolutio

BIGSdb: Scalable analysis of bacterial genome variation at the population level

<p>Abstract</p> <p>Background</p> <p>The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner.</p> <p>Results</p> <p>The Bacterial Isolate Genome Sequence Database (BIGS<smcaps>DB</smcaps>) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGS<smcaps>DB</smcaps> are illustrated with the genera <it>Neisseria </it>and <it>Streptococcus</it>.</p> <p>The BIGS<smcaps>DB</smcaps> source code and documentation are available at <url>http://pubmlst.org/software/database/bigsdb/</url>.</p> <p>Conclusions</p> <p>Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGS<smcaps>DB</smcaps> represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.</p