1887

Microbial Genomics logo

Volume 9, Issue 8

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia Open Access

Abstract

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): Bayesian phylogeography , COVID-19 , genomics , metadata , public health and SARS-CoV-2
Funding
This study was supported by the:
  • National Health and Medical Research Council Australia (Award GNT1196103)
    • Principle Award Recipient: BenjaminP Howden
  • Australian Research Council (Award DE190100805)
    • Principle Award Recipient: SebastianDuchene
  • National Health and Medical Research Council, Medical Research Future Fund (Award MRF9200006)
    • Principle Award Recipient: NotApplicable
© 2023 Crown Copyright
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001099
2023-08-31
2024-04-30
Loading full text...

Full text loading...

/deliver/fulltext/mgen/9/8/mgen001099.html?itemId=/content/journal/mgen/10.1099/mgen.0.001099&mimeType=html&fmt=ahah

References

  1. Mavian C, Marini S, Prosperi M, Salemi M. A snapshot of SARS-CoV-2 genome availability up to april 2020 and its implications: data analysis. JMIR Public Health Surveill 2020; 6:e19170 [View Article] [PubMed]
     [Google Scholar]
  2. Hill V, Ruis C, Bajaj S, Pybus OG, Kraemer MUG. Progress and challenges in virus genomic epidemiology. Trends Parasitol 2021; 37:1038–1049 [View Article] [PubMed]
     [Google Scholar]
  3. Hodcroft EB, Zuber M, Nadeau S, Vaughan TG, Crawford KHD et al. Spread of a SARS-CoV-2 variant through Europe in the summer of 2020. Nature 2021; 595:707–712 [View Article] [PubMed]
     [Google Scholar]
  4. Porter AF, Sherry N, Andersson P, Johnson SA, Duchene S et al. New rules for genomics-informed COVID-19 responses-lessons learned from the first waves of the Omicron variant in Australia. PLoS Genet 2022; 18:e1010415 [View Article] [PubMed]
     [Google Scholar]
  5. Lane CR, Sherry NL, Porter AF, Duchene S, Horan K et al. Genomics-informed responses in the elimination of COVID-19 in Victoria, Australia: an observational, genomic epidemiological study. Lancet Public Health 2021; 6:e547–e556 [View Article] [PubMed]
     [Google Scholar]
  6. Australian Government Department of Health and Aged Care. COVID-19 at a glance; 2020 https://www.health.gov.au/resources/publications/coronavirus-covid-19-at-a-glance-2-december-2020 accessed 19 August 2022
  7. Bedford T, Greninger AL, Roychoudhury P, Starita LM, Famulare M et al. Cryptic transmission of SARS-CoV-2 in Washington state. Science 2020; 370:571–575 [View Article] [PubMed]
     [Google Scholar]
  8. Grenfell BT, Pybus OG, Gog JR, Wood JLN, Daly JM et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 2004; 303:327–332 [View Article] [PubMed]
     [Google Scholar]
  9. Baele G, Suchard MA, Rambaut A, Lemey P. Emerging concepts of data integration in pathogen phylodynamics. Syst Biol 2017; 66:e47–e65 [View Article] [PubMed]
     [Google Scholar]
  10. Lemey P, Hong SL, Hill V, Baele G, Poletto C et al. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nat Commun 2020; 11:1–14 [View Article] [PubMed]
     [Google Scholar]
  11. Ehichioya DU, Dellicour S, Pahlmann M, Rieger T, Oestereich L et al. Phylogeography of lassa virus in Nigeria. J Virol 2019; 93:e00929-19 [View Article] [PubMed]
     [Google Scholar]
  12. Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet 2022; 23:547–562 [View Article] [PubMed]
     [Google Scholar]
  13. Population-based analysis of the epidemiological features of COVID-19 epidemics in Victoria, Australia, January 2020 - March 2021, and their suppression through comprehensive control strategies. Lancet Reg Health West Pac 2021; 17:100297 [View Article] [PubMed]
     [Google Scholar]
  14. Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall 2017; 1:33–46 [View Article] [PubMed]
     [Google Scholar]
  15. Shu Y, McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality. Euro Surveill 2017; 22:30494 [View Article] [PubMed]
     [Google Scholar]
  16. Wirth W, Duchene S. GISAIDR 2021 https://zenodo.org/record/6474693
  17. Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 2002; 30:3059–3066 [View Article] [PubMed]
     [Google Scholar]
  18. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013; 30:772–780 [View Article] [PubMed]
     [Google Scholar]
  19. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 2020; 37:1530–1534 [View Article] [PubMed]
     [Google Scholar]
  20. Hasegawa M, Kishino H, Yano T. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 1985; 22:160–174 [View Article] [PubMed]
     [Google Scholar]
  21. Duchene S, Featherstone L, Haritopoulou-Sinanidou M, Rambaut A, Lemey P et al. Temporal signal and the phylodynamic threshold of SARS-CoV-2. Virus Evol 2020; 6:veaa061 [View Article] [PubMed]
     [Google Scholar]
  22. Rambaut A, Lam TT, Max Carvalho L, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol 2016; 2:vew007 [View Article] [PubMed]
     [Google Scholar]
  23. Ren L-L, Wang Y-M, Wu Z-Q, Xiang Z-C, Guo L et al. Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study. Chin Med J 2020; 133:1015–1024 [View Article] [PubMed]
     [Google Scholar]
  24. Duchene S, Featherstone L, Freiesleben de Blasio B, Holmes EC, Bohlin J et al. The impact of public health interventions in the Nordic countries during the first year of SARS-CoV-2 transmission and evolution. Euro Surveill 2021; 26:44 [View Article] [PubMed]
     [Google Scholar]
  25. Lemey P, Rambaut A, Drummond AJ, Suchard MA. Bayesian phylogeography finds its roots. PLoS Comput Biol 2009; 5:e1000520 [View Article] [PubMed]
     [Google Scholar]
  26. Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst Biol 2018; 67:901–904 [View Article] [PubMed]
     [Google Scholar]
  27. Australia Parliament COVID-19: a chronology of State and territory government announcements (up until 30 June 2020). Commonwealth of Australia; 2020
  28. Hugo G, Rudd D, Harris K. Australia’s Diaspora: Its Size, Nature and Policy Implications CEDA Information Paper; 2003 https://www.ceda.com.au/ResearchAndPolicies/Research/Population/Information-Paper-80- Australia-s-Diaspora-Its-Size
  29. Seemann T, Lane CR, Sherry NL, Duchene S, Gonçalves da Silva A et al. Tracking the COVID-19 pandemic in Australia using genomics. Nat Commun 2020; 11:4376 [View Article] [PubMed]
     [Google Scholar]
  30. Mallapaty S. Omicron-variant border bans ignore the evidence, say scientists. Nature 2021; 600:199 [View Article] [PubMed]
     [Google Scholar]
  31. Mathieu E, Ritchie H, Rodés-Guirao L, Appel C, Giattino C et al. Coronavirus pandemic (COVID-19): published online at OurWorldInData.org; 2020 https://ourworldindata.org/coronavirus
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.001099
Loading
The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia
M Gen 9, 001099 (2023); https://doi.org/10.1099/mgen.0.001099
/content/journal/mgen/10.1099/mgen.0.001099
/content/journal/mgen/10.1099/mgen.0.001099
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error