ABSTRACT
BACKGROUND: Faced to the ongoing global pandemic of coronavirus disease, the 'National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis' (GENPAT) formally established at the 'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise' (IZSAM) in Teramo (Italy) supports the genomic surveillance of the SARS-CoV-2. In a context of SARS-CoV-2 surveillance needed proper and fast assessment of epidemiological clusters from large amount of samples, the present manuscript proposes a workflow for identifying accurately the PANGOLIN lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model. RESULTS: GENPAT constituted two collections of SARS-CoV-2 samples. The samples of the first collection were isolated by IZSAM in the Abruzzo region (Italy), then shotgun sequenced and analyzed in GENPAT (n = 1 592), while those of the second collection were isolated from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17 201). The main outcomes of the present study showed that (i) GENPAT and GISAID identified identical PANGOLIN lineages, (ii) the PANGOLIN lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. 'UK variant') are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGOLIN lineages together, (iv) with a higher dicriminatory power than PANGOLIN, (v) and faster that the usual phylogenomic methods based on MSA and substitution model. CONCLUSIONS: The shotgun sequencing efforts of Italian provinces, combined to a structured national system of metagenomics data management, provided support for surveillance SARS-CoV-2 in Italy. We recommend to infer phylogenomic relationships of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method bypassing the usual time consuming steps related to MSA and substitution model-based phylogenomic inference.
Subject(s)
Coronavirus InfectionsABSTRACT
Italy’s second wave of SARS-CoV-2 has hit hard, with more than 3 million cases and over 100,000 deaths, representing an almost ten-fold increase on the numbers reported by August 2020. Herein, we present the analysis of 6,515 SARS-CoV-2 sequences sampled in Italy between 29 th January 2020 and 1 st March 2021 and show how different lineages emerged multiple times independently despite lockdown restrictions. Virus lineage B.1.177 became the dominant variant in November 2020, when cases peaked at 40,000 a day, but since January 2021 this is being replaced by the B.1.1.7 ‘variant of concern’. In addition, we report a sudden increase in another documented variant of concern – lineage P.1 – from December 2020 onwards, most likely caused by a single introduction into Italy. We again highlight how international importations drive the emergence of new lineages and that genome sequencing should remain a top priority for ongoing surveillance in Italy.
ABSTRACT
In February 2020, Italy became the epicentre for COVID-19 in Europe and at the beginning of March, in response to the growing epidemic, the Italian Government put in place emergency measures to restrict the movement of the population. Human mobility represents a crucial element to be considered in modelling human infectious diseases. In this paper, we examined the mechanisms underlying COVID-19 propagation using a Susceptible-Infected stochastic model (SI) driven mainly by commuting network in Italy. We modelled a municipality-specific contact rate to capture the disease permeability of each municipality, considering the population at different times of the day and describing the characteristic of the municipalities as attractors of commuters or places that make their workforce available elsewhere. The purpose of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy and to characterize the territory in terms of vulnerability at local or national level. The use of data at such a high spatial resolution allows highlighting particular situations on which the health authorities can promptly intervene to control the disease spread. Our approach provides decision-makers with useful geographically detailed metrics to evaluate those areas at major risk for infection spreading and for which restrictions of human mobility would give the greatest benefits, not only at the beginning of the epidemic but also in the last phase, when the risks deriving from the gradual lockdown exit strategies must be carefully evaluated.