The early dynamics of the SARS-CoV-2 epidemic in Portugal
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Abstract
Background
Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. This unprecedented collaborative effort culminated in the generation of 1275 SARS-CoV-2 genome sequences, which represent 15.5% of all confirmed cases in March 2020, making Portugal one of the countries generating the highest volumes of SARS-CoV-2 genomic data during early COVID-19 pandemic.
Methods
We reconstructed and characterized the spatio-temporal dynamics of SARS-CoV-2 introductions and early dissemination in Portugal using recent phylodynamic models that allow integration of individual-based travel history, in order to obtain a more realistic reconstruction of the viral dynamics.
Results
We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy and Switzerland), which was broadly consistent with the available travel history data, as well as with the countries with most frequent connectivity and/or with the highest number of Portuguese immigrants. Although most introductions were estimated to have occurred during the last week of February and the first week of March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal several weeks before the first confirmed local cases on March 2, 2020.
Discussion and Conclusion
While the implemented preventive and early control measures seem to have been successful in mitigating community transmission from most independent introductions, our results suggest that their earlier implementation could have largely minimized the number of introductions and subsequent virus expansion. Here we lay the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlight the need for systematic, continuous and geographically-representative genomic surveillance to guide national and international public health authorities toward the characterization and control of SARS-CoV-2 circulating diversity.
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SciScore for 10.1101/2021.02.22.21252216: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Pooling, denaturation and dilution of bead-based normalized libraries was performed according to the manufacturer’s instructions for the MiSeq or NextSeq 550 systems (Illumina). MiSeqsuggested: (A5-miseq, RRID:SCR_012148)Briefly, the core bioinformatics steps (documented in Borges et al, 2018 and https://insaflu.readthedocs.io/) involved: i) raw NGS reads quality analysis and improvement using FastQC; (https://www.bioinformatics.babraham.ac.uk/projects/fastqc) and Trimmomatic (http://www.usadellab.org/cms/index.php?page=trimmomatic), respectively (read’s ends were cropped 30bp for primer … SciScore for 10.1101/2021.02.22.21252216: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Pooling, denaturation and dilution of bead-based normalized libraries was performed according to the manufacturer’s instructions for the MiSeq or NextSeq 550 systems (Illumina). MiSeqsuggested: (A5-miseq, RRID:SCR_012148)Briefly, the core bioinformatics steps (documented in Borges et al, 2018 and https://insaflu.readthedocs.io/) involved: i) raw NGS reads quality analysis and improvement using FastQC; (https://www.bioinformatics.babraham.ac.uk/projects/fastqc) and Trimmomatic (http://www.usadellab.org/cms/index.php?page=trimmomatic), respectively (read’s ends were cropped 30bp for primer clipping); ii) draft de novo assembly using SPAdes (http://cab.spbu.ru/software/spades/) followed by classification and contigs assignment of Human Betacoronavirus; and, iii) reference-based mapping, consensus generation and mutation detection using the multisoftware tool Snippy (https://github.com/tseemann/snippy), using the Wuhan-Hu-1/2019 genome sequence (https://www.ncbi.nlm.nih.gov/nuccore/MN908947) as reference. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)SPAdessuggested: (SPAdes, RRID:SCR_000131)Multiple sequence alignments with a reference genome (MN908947.3) were performed using MAFFT v7.458 with parameter --addfragments (Katoh et al, 2009). MAFFTsuggested: (MAFFT, RRID:SCR_011811)To further address this, we have built a phylogeny using IQ-TREE, as described previously, and partitioned the tree in 6 monophyletic clades (B_CS1 through B_CS6). IQ-TREEsuggested: (IQ-TREE, RRID:SCR_017254)These clades were examined for outlier sequences whose genetic divergence and sampling date were incongruent using TempEst software version 1.5.2 (Rambaut et al, 2016). TempEstsuggested: (TempEst, RRID:SCR_017304)For each of the datasets (A, and B_CS1 through B_CS6), we performed a joint genealogical and phylogeographic inference of time-measured trees using Markov chain Monte Carlo (MCMC) sampling implemented in the Bayesian Evolutionary Analysis Sampling Trees (BEAST) package (Suchard et al, 2018). BEASTsuggested: (BEAST, RRID:SCR_010228)Stationarity and mixing was investigated using Tracer software version 1.7.1 (Rambaut et al, 2018), making sure that effective sample sizes for the continuous parameters were greater than 100. Tracersuggested: (Tracer, RRID:SCR_019121)Real-time data sharing of SARS-CoV-2 genetic diversity and geotemporal spread in Portugal: A website (https://insaflu.insa.pt/covid19) was launched on March 28, 2020 for real-time data sharing on SARS-CoV-2 genetic diversity and geotemporal spread in Portugal. Portugalsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One limitation of this and other studies trying to infer the origin of introductions is the sequencing sampling bias by country. For this reason, we resorted to filtering and subsampling strategies that aim at reducing the number of genomes that do not significantly contribute to the evolutionary or phylogeographic reconstructions. Despite being less pronounced, there were still discrepancies in the number of genomes included across countries. For instance, it is very likely that the number of introductions via the UK (the country generating the highest volume of sequences) is overestimated, while the number of introductions from countries with none or few available genome sequences is underestimated. However, the integration of travel history during the phylodynamic inferences allows for a more accurate reconstruction of the spatial pathways from both over and undersampled locations. Additionally, using this recently developed model provides insight into the genetic diversity circulating in countries for which genomic surveillance is still lacking. This makes the present work among the first to apply this novel model, which allowed us to gain insight into the early circulating diversity in countries like Saint Thomas and Prince, for which (as of February 15, 2021) there are no sequences available, and other undersampled countries such as the United Arabe Emirates and Qatar. Overall, our findings highlight the use of genomic data to trace the introduction and spread of an eme...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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