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

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.

Keywords: Australia; COVID-19; Iceland; New Zealand; Taiwan; contact tracing; coronavirus; human movement; phylogenomics.

Figure 1.

Depiction of the MTBD model. (A) A full infection tree and a sampled infection tree (T), with two time intervals (epochs) and two demes (indicated in grey and red). All lineages in the trees represent infected individuals (I). Branching corresponds to transmission and colour change (arrows) corresponds to migration; infected individuals may be removed (R) or sampled and then removed (S). (B) Transition diagram: solid and dashed arrows labelled with rates and probabilities, respectively.

Figure 2.

Human walking movement relative to Day 1 (100 per cent). Data were obtained by tracking the mobility of Apple mobile phone users (Apple 2020). Top: movement data from Apple. Bottom: fitted sigmoid model (solid lines show the median and shaded curves show the 2.5–97.5 per cent quantile interval); vertical dotted lines correspond to the estimated mean date of mobility change, which defines the t₂ epoch date for each deme.

Figure 3.

R_e prior (white) and posterior (blue) probability distributions across islands and time intervals (n samples in each interval). Posterior means are indicated by dashed lines. Mean estimates (and 95 per cent highest posterior density intervals) are indicated under each time interval: t₁ is the first case in the target deme, t₂ the start of mobility reduction, and t_f the final sample in the data set.

Figure 4.

(A) New Zealand COVID-19 maximum-clade-credibility tree (from ‘small-time’ alignment). Samples are labelled according to outbreaks identified by NZMH. Arrows indicate cases assigned to a cluster by NZMH, which are not monophyletic with the rest of the cluster. (B) Number of SARS-CoV-2 introductions in New Zealand over time. (C) New Zealand cluster locations (with greater than five cases).

Figure 5.

Maximum-clade-credibility COVID-19 tree (from ‘small-time’ alignment) for Australia (top), Iceland (middle), and Taiwan (bottom). Plots show the estimated number of SARS-CoV-2 introductions over time.