2 research outputs found
SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal
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.
Methods By applying recent phylodynamic models that allow integration of individual-based
travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal.
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 were consistent with the countries with the highest connectivity with Portugal.
Although most introductions were estimated to have occurred during early March 2020, it is
likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the
first cases were confirmed.
Conclusions Here we conclude that the earlier implementation of measures could have
minimized the number of introductions and subsequent virus expansion in Portugal. This
study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and
Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with
the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team,
IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation
(https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing
guidance on the implementation of the phylodynamic models; Joshua L. Cherry
(National Center for Biotechnology Information, National Library of Medicine, National
Institutes of Health) for providing guidance with the subsampling strategies; and all
authors, originating and submitting laboratories who have contributed genome data on
GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions
expressed in this article are those of the authors and do not reflect the view of the
National Institutes of Health, the Department of Health and Human Services, or the
United States government. This study is co-funded by Fundação para a Ciência e Tecnologia
and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on
behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study
come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by
COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation
(POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal
Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL
2020 Partnership Agreement, through the European Regional Development Fund
(ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio
SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling.
Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty.
Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year.
Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population