[en] COVID-19 transmission rates are often linked to locally circulating strains of SARS-CoV-2. Here we describe 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in Rwanda from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the B.1.1.7 and B.1.351 variants of concern among incoming travelers tested at Kigali International Airport. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences were available. Our results highlight the importance of systematic genomic surveillance and regional collaborations for a durable response towards combating COVID-19.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Rambaut, A. et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat. Microbiol. 10.1038/s41564-020-0770-5 (2020)
Volz, E. et al. Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: insights from linking epidemiological and genetic data. medRxiv https://doi.org/10.1101/2020.12.30.20249034 (2021).
Horby, P. et al. NERVTAG note on B.1.1.7 severity. SAGE https://www.gov.uk/government/publications/nervtag-paper-on-covid-19-variant-of-concern-b117 (2021).
Davies, N. G. et al. Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7. Nature https://doi.org/10.1038/s41586-021-03426-1 (2021).
Cele, S. et al. Escape of SARS–CoV-2 501Y.V2 variants from neutralization by convalescent plasma. medRxiv https://doi.org/10.1038/s41586-021-03471-w (2021).
Mutesa, L. et al. A pooled testing strategy for identifying SARS–CoV-2 at low prevalence. Nature https://doi.org/10.1038/s41586-020-2885-5 (2020).
Clarisse, M. et al. Use of technologies in COVID-19 containment in Rwanda. Rw. Public Health Bul. 2, 7–12 (2020).
Musanabagnwa, C. et al. Easing lockdown restrictions during COVID-19 outbreak in Rwanda. Rw. Public Health Bul. 2, 24–29 (2020).
Bugembe, D. L. et al. Emergence and spread of a SARS–CoV-2 lineage a variant (A.23.1) with altered spike protein in Uganda. Nat. Microbiol. 10.1038/s41564-021-00933-9 (2021).
Zhang, L. et al. SARS–CoV-2 spike-protein D614G mutation increases virion spike density and infectivity. Nat. Commun. https://doi.org/10.1038/s41467-020-19808-4 (2020).
Elbe, S. & Buckland-Merrett, G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob. Chall. 1, 33–46 (2017). DOI: 10.1002/gch2.1018
Shu, Y. & Mccauley, J. GISAID: Global initiative on sharing all influenza data – from vision to reality. Euro Surveill. https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494. (2017).
WHO. WHO Coronavirus (COVID-19) dashboard. WHO Web https://www.who.int/emergencies/diseases/novel-coronavirus-2019?adgroupsurvey={adgroupsurvey}&gclid=Cj0KCQjwnJaKBhDgARIs AHmvz6cWRpag13 Yhl0uOTpDqqBkygPrd- 6E7YVWisNDHwSmdet86IrizrTIaArDrEALw_wcB (2020).
Lemey, P. et al. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS–CoV-2. Nat. Commun. 11, 1–14 (2020). DOI: 10.1038/s41467-020-18877-9
Hong, S., Lemey, P., Suchard, M., Baele, & G. Bayesian phylogeographic analysis incorporating predictors and individual travel histories in BEAST. Curr Protoc. https://doi.org/10.1002/cpz1.98. PMID: 33836121 (2021).
Parker, J., Rambaut, A. & Pybus, O. G. Correlating viral phenotypes with phylogeny: accounting for phylogenetic uncertainty. Infect. Genet. Evol. 8, 239–246 (2008). DOI: 10.1016/j.meegid.2007.08.001
WHO. WHO Director General’s Statement on Tanzania and COVID-19. https://www.who.int/news/item/20-02-2021-who-director-general-s-statement-on-tanzania-and-covid-19 (2021).
Ferguson, Neil M et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team, London, https://doi.org/10.25561/77482 (2020).
Supasa, P. et al. Reduced neutralization of SARS–CoV-2 B.1.1.7 variant from naturally acquired and vaccine induced antibody immunity. SSRN https://doi.org/10.2139/ssrn.3775873 (2021).
Zhou, D., Supasa, P., Ren, J., Stuart, D. I. & Screaton, G. R. Article evidence of escape of SARS–CoV-2 variant B. 1. 351 from natural and vaccine-induced sera ll ll evidence of escape of SARS-CoV-2 variant B. 1. 351 from natural and vaccine-induced sera. Cell 184, 2348–2361 (2021). DOI: 10.1016/j.cell.2021.02.037
Becker, M. et al. Immune response to SARS–CoV-2 variants of concern in vaccinated individuals. Nat. Commun. 10.1038/s41467-021-23473-6 (2021).
Freed, N. E., Vlková, M., Faisal, M. B. & Silander, O. K. Rapid and inexpensive whole-genome sequencing of SARS-CoV-2 using 1200 bp tiled amplicons and Oxford Nanopore Rapid Barcoding. Biol. Methods Protoc. 5, 1–7 (2021).
Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018). DOI: 10.1093/bioinformatics/bty407
Toole, Á. O. et al. Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol. 07, 1–9 (2021).
Bruls, M., Huizing, K., van Wijk, J. J. Eurographics (Springer, 2000).
Li, H. Sequence analysis Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018). DOI: 10.1093/bioinformatics/bty191
Sagulenko, P., Puller, V. & Neher, R. A. TreeTime: maximum-likelihood phylodynamic analysis. Virus Evol. 4, 1–9 (2018). DOI: 10.1093/ve/vex042
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1. 10. Virus Evol. 4, 1–5 (2018). DOI: 10.1093/ve/vey016
Lauer, S. A. et al. The incubation period of coronavirus disease 2019 (CoVID-19) from publicly reported confirmed cases: estimation and application. Ann. Intern. Med. 172, 577–582 (2020). DOI: 10.7326/M20-0504
Yres, D. A. L. A. et al. BEAGLE 3: Improved performance, scaling, and usability for a high-performance computing library for statistical phylogenetics. Softw. Syst. Evolution 68, 1052–1061 (2019).
Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1000520 (2009).
Minin, V. N. & Suchard, M. A. Counting labeled transitions in continuous-time Markov models of evolution. J. Math. Biol. 56, 391–412 (2008). DOI: 10.1007/s00285-007-0120-8
Rambaut, A., DRummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer. Syst. Biol. 67, 901–904 (2018).
Dellicour, S. et al. A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS–CoV-2 lineages. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa284 (2020).
Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evolution 27, 1877–1885 (2010). DOI: 10.1093/molbev/msq067
Dellicour, S., Rose, R., Faria, N. R., Lemey, P. & Pybus, O. G. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016). DOI: 10.1093/bioinformatics/btw384
Dudas, G. & Rambaut, A. MERS-CoV recombination: implications about the reservoir and potential for adaptation.Virus Evol. 2, 1–11 (2016). DOI: 10.1093/ve/vev023
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.