A Metric on Phylogenetic Tree Shapes

The shapes of evolutionary trees are influenced by the nature of the evolutionary process but comparisons of trees from different processes are hindered by the challenge of completely describing tree shape. We present a full characterization of the shapes of rooted branching trees in a form that lends itself to natural tree comparisons. We use this characterization to define a metric, in the sense of a true distance function, on tree shapes. The metric distinguishes trees from random models known to produce different tree shapes. It separates trees derived from tropical versus USA influenza A sequences, which reflect the differing epidemiology of tropical and seasonal flu. We describe several metrics based on the same core characterization, and illustrate how to extend the metric to incorporate trees’ branch lengths or other features such as overall imbalance. Our approach allows us to construct addition and multiplication on trees, and to create a convex metric on tree shapes which formally allows computation of average tree shapes.

Erratum to: Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA)

[This corrects the article DOI: 10.1186/s13017-016-0089-y.].

Respondent driven sampling and community structure in a population of injecting drug users, Bristol, UK

BACKGROUND

A 2006 respondent driven sampling (RDS) survey of injecting drug users (IDUs) in Bristol, UK, estimated 40 per 100 person years HCV incidence but in 2009 another RDS survey estimated only 10 per 100 person years incidence amongst the same population. Estimated increases in intervention exposure do not fully explain the decrease in risk. We investigate whether the underlying contact network structure and differences in the structure of the RDS trees could have contributed to the apparent change in incidence.

METHOD

We analyse the samples for evidence that individuals recruit participants who are like themselves (assortative recruiting). Using an assortativity measure, we develop a Monte Carlo approach to determine whether the RDS data exhibit significantly more assortativity than is expected for that sample. Motivated by these findings, a network model is used to investigate how much assortativity and the structure of the RDS tree impacts sample estimates of prevalence and incidence.

RESULTS

The samples suggest there is some assortativity on injecting habits or markers of injecting risk. The 2009 sample has lower assortativity than 2006. Simulations of RDS confirm that assortativity influences the estimated incidence in a population and the structure of RDS samples can result in bias. Our simulations suggest that RDS incidence estimates have considerable variance, making them difficult to use for monitoring trends.

CONCLUSIONS

We suggest there was likely to have been a decline in risk between 2006 and 2009 due to increased intervention coverage, but the bias and variance in the estimates prevents accurate estimation of the incidence.

Modelling the performance of isoniazid preventive therapy for reducing tuberculosis in HIV endemic settings: the effects of network structure

Individuals living with HIV experience a much higher risk of progression from latent M. tuberculosis infection to active tuberculosis (TB) disease relative to individuals with intact immune systems. A several-month daily course of a single drug during latent infection (i.e. isoniazid preventive therapy (IPT)) has proved in clinical trials to substantially reduce an HIV-infected individual's risk of TB disease. As a result of these findings and ongoing studies, the World Health Organization has produced strong guidelines for implementing IPT on a community-wide scale for individuals with HIV at risk of TB disease. To date, there has been limited use of IPT at a community-wide level. In this paper, we present a new co-network model for HIV and TB co-epidemics to address questions about how the population-level impact of community-wide IPT may differ from the individual-level impact of IPT offered to selected individuals. In particular, we examine how the effect of clustering of contacts within high-TB incidence communities may affect the rates of re-infection with TB and how this clustering modifies the expected population-level effects of IPT. We find that populations with clustering of respiratory contacts experience aggregation of TB cases and high numbers of re-infection events. While, encouragingly, the overall population-level effects of community-wide IPT appear to be sustained regardless of network structure, we find that in populations where these contacts are highly clustered, there is dramatic heterogeneity in the impact of IPT: in some sub-regions of these populations, TB is nearly eliminated, while in others, repeated re-infection almost completely undermines the effect of IPT. Our findings imply that as IPT programmes are brought to scale, we should expect local heterogeneity of effectiveness as a result of the complex patterns of disease transmission within communities.