Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations

  1. Jordan Douglas
  2. Fábio K Mendes
  3. Remco Bouckaert
  4. Dong Xie
  5. Cinthy L Jiménez-Silva
  6. Christiaan Swanepoel
  7. Joep de Ligt
  8. Xiaoyun Ren
  9. Matt Storey
  10. James Hadfield
  11. Colin R Simpson
  12. Jemma L Geoghegan
  13. Alexei J Drummond
  14. David Welch

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Abstract

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1–1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

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  1. Version published to 10.1093/ve/veab052
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    SciScore for 10.1101/2020.08.04.20168518: (What is this?)

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    Table 2: Resources

    No key resources detected.

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    Nonetheless, contact tracing has its limitations. Establishing the link between cases is re19 stricted, for example, by the ability of cases to recall their recent contacts and travel history, which can lead to type-2 mismatches between epidemiological and genetic clusters. Furthermore, a case may have attended an event causally linked to a cluster but acquired their infection elsewhere and did not infect anyone at the event (type-1 mismatches). The implications of these types of error are manifold. If cluster sizes are underestimated, then so too is the rate of disease spread. If the extent of import-related cases is overestimated, then the impact of international air travel and the extent of community transmission cannot be fully accounted for. After considering 217 genomes (representing 19% of the cases in New Zealand at the time), our results suggest there are an additional 18 unclassified cases in New Zealand that could have been linked to known clusters but were not (type-2 mismatch), and 5 cases that were linked to a cluster where they did not acquire or transmit the infection (type-1). However, by and large, our phylodynamic analysis is in agreement with conclusions reached by NZMH. Overall, we have shown that contact tracing has been accurate among the cases considered, and succeeded to a large degree in identifying individuals belonging to the same infection cluster. We have demonstrated how the rapid real-time availability and assessment of viral genomic data can c...

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    • 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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  3. Version published to 10.1101/2020.08.04.20168518 on medRxiv