Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model.

BACKGROUND: SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France. METHODS: In this paper we investigate the spreading potential of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants' transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations. RESULTS: Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and an omicron generation time of 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, [Formula: see text], was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led to [Formula: see text] ranging from 1.57 to 2.34 on average over the metropolitan French regions, weighted by population size. CONCLUSIONS: Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events.

Improving distance measures between genomic tracks with mutual proximity.

An increasing number of genomic tracks such as DNA methylation, histone modifications or transcriptomes are being produced to annotate genomes with functional states. The comparison of such high dimensional vectors obtained under various experimental conditions requires the use of a distance or dissimilarity measure. Pearson, Cosine and $L_{p}$-norm distances are commonly used for both count and binary vectors. In this article, we highlight how enhancement methods such as the contrast increasing mutual proximity' (MP) or local scaling' improve common distance measures. We present a systematic approach to evaluate the performance of such enhanced distance measures in terms of separability of groups of experimental replicates to outline their effect. We show that the MP' applied on the various distance measures drastically increases performance. Depending on the type of epigenetic experiment, MP' coupled together with Pearson, Cosine, $L_1$, Yule or Jaccard distances proves to be highly efficient in discriminating epigenomic profiles.

MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences.

SUMMARY: Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences. AVAILABILITY AND IMPLEMENTATION: Open source with a Zlib License via the Git protocol: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools. MANUAL: A detailed users guide and tutorial: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf. WEBSITE AND FAQ: http://treetools.haschka.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.