Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep .

Outbreak investigation for toxigenic Corynebacterium diphtheriae wound infections in refugees from Northeast Africa and Syria in Switzerland and Germany by whole genome sequencing.

Toxigenic Corynebacterium diphtheriae is an important and potentially fatal threat to patients and public health. During the current dramatic influx of refugees into Europe, our objective was to use whole genome sequencing for the characterization of a suspected outbreak of C. diphtheriae wound infections among refugees. After conventional culture, we identified C. diphtheriae using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and investigated toxigenicity by PCR. Whole genome sequencing was performed on a MiSeq Illumina with >70×coverage, 2×250 bp read length, and mapping against a reference genome. Twenty cases of cutaneous C. diphtheriae in refugees from East African countries and Syria identified between April and August 2015 were included. Patients presented with wound infections shortly after arrival in Switzerland and Germany. Toxin production was detected in 9/20 (45%) isolates. Whole genome sequencing-based typing revealed relatedness between isolates using neighbour-joining algorithms. We detected three separate clusters among epidemiologically related refugees. Although the isolates within a cluster showed strong relatedness, isolates differed by >50 nucleotide polymorphisms. Toxigenic C. diphtheriae associated wound infections are currently observed more frequently in Europe, due to refugees travelling under poor hygienic conditions. Close genetic relatedness of C. diphtheriae isolates from 20 refugees with wound infections indicates likely transmission between patients. However, the diversity within each cluster and phylogenetic time-tree analysis suggest that transmissions happened several months ago, most likely outside Europe. Whole genome sequencing offers the potential to describe outbreaks at very high resolution and is a helpful tool in infection tracking and identification of transmission routes.