Phylodynamic applications in 21 century global infectious disease research.

Background: Phylodynamics, the study of the interaction between epidemiological and pathogen evolutionary processes within and among populations, was originally defined in the context of rapidly evolving viruses and used to characterize transmission dynamics. The concept of phylodynamics has evolved since the early 21 century, extending its reach to slower-evolving pathogens, including bacteria and fungi, and to the identification of influential factors in disease spread and pathogen population dynamics.

Results: The phylodynamic approach has now become a fundamental building block for the development of comparative phylogenetic tools capable of incorporating epidemiological surveillance data with molecular sequences into a single statistical framework.

Predicting phenotypes of asthma and eczema with machine learning.

Background: There is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations.