Combining genomics and epidemiology to analyse bi-directional transmission of Mycobacterium bovis in a multi-host system.

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, Mycobacterium bovis, persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in M. bovis infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than vice versa (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.

Epidemics on dynamic networks.

In many populations, the patterns of potentially infectious contacts are transients that can be described as a network with dynamic links. The relative timescales of link and contagion dynamics and the characteristics that drive their tempos can lead to important differences to the static case. Here, we propose some essential nomenclature for their analysis, and then review the relevant literature. We describe recent advances in they apply to infection processes, considering all of the methods used to record, measure and analyse them, and their implications for disease transmission. Finally, we outline some key challenges and opportunities in the field.

Tools to study trends in community structure: application to fish and livestock trading networks.

Partitioning of contact networks into communities allows groupings of epidemiologically related nodes to be derived, that could inform the design of disease surveillance and control strategies, e.g. contact tracing or design of 'firebreaks' for disease spread. However, these are only of merit if they persist longer than the timescale of interventions. Here, we apply different methods to identify concordance between network partitions across time for two animal trading networks, those of salmon in Scotland (2002-2004) and livestock in Great Britain (2003-2004). Both trading networks are similar in that they moderately agree over time in terms of their community structures, but this concordance is higher--and therefore community structure is more consistent--when only the 'core' network of nodes involved in trading over the whole time series is considered. In neither case was higher agreement found between partitions close together in time. These measures differ in their absolute values unless appropriate standardisation is applied. Once standardised, the measures gave similar values for both network types.