The differential importation risks of COVID-19 from inbound travellers and the feasibility of targeted travel controls: A case study in Hong Kong

Abstract

Background: Many countries/regions implemented strict border measures (e.g., 14-day quarantines) as a blanket policy to prevent COVID-19 importations, while proposed "travel bubbles" as an alternative to reduce the impact of border controls. We aim to examine the differential importation risks with departure origins and post-arrival controls.

Methods: We developed a Bayesian framework to model disease progress of COVID-19 and the effectiveness of travel measures and inferred the origin-specific disease prevalence among inbound travellers, using data on passengers arriving in Hong Kong and laboratory-confirmed imported cases. We estimated the origin-specific risks of releasing infectious travellers under different control strategies and traveller volumes. We also estimated the risk of having released infectious travellers when a resurgence occurs in departure locations with no imported cases during a certain period.

Findings: Under the then strict controls of 14-day quarantine and testing on day 12, the Philippines imposed the greatest importation risk among the studied countries/regions (95.8% of releasing at least one infectious traveller, 95% credible interval (CrI), 94.8-96.6%). This was higher than that from low prevalence countries/regions (e.g., 23.4%, 95% CrI, 21.6-25.3% for Taiwan) if controls relaxed (i.e., 7-day quarantine and test on day 5). Increased traveller volumes and resurgence in departure locations with low prevalence under relaxed controls did not impose a greater importation risk than high prevalence locations under stricter controls.

Interpretation: Moderate relaxation of control measures for travellers arriving from low prevalence locations did not impose higher risks of community outbreaks than strict controls on travellers from high prevalence locations.

Funding: Health and Medical Research Fund, Hong Kong.

Figure 1

Travel controls and the natural history of COVID-19. (A) Example travel control process and possible steps where infected travellers could be identified. Dark and light red indicate symptomatic (including who show symptoms post arrival) and asymptomatic individuals, respectively. Grey bordered figures indicate the infected individual is no longer infectious. The released infectious travellers were indicated as red texts (same for panel B), which are the central outcome that was modelled in this study. (B) Representative individual infectious profiles by peak viral load. We assumed symptoms onset coincides with peak viral load for symptomatic cases. (C) Incubation period of COVID-19. Data were derived from He et al.(12)

Figure 2

Observed temporal distribution of imported COVID-19 cases (A) and distribution of the on-arrival tests (B) from the eight representative countries and regions between April to July, 2020. In panel B, blue bars represent the number of inbound travellers arriving Hong Kong; the red points and red lines indicate the mean and 95% CI of inbound travellers that tested positive on arrival.

Figure 3

Expected importation risks of infectious travellers under different quarantine control strategies. Traveller volumes and prevalence of COVID-19 among inbound travellers that were estimated from actual data were used in the simulations. (A) The number of infectious travellers who were released to the community. Median (thick horizontal tick), interquartile (shaded rectangles) and 95% quantiles (solid vertical lines) of 2,000 simulations were shown. (B) The risk of releasing at least one infectious traveller to the community. Mean (dots) and 95% CI (vertical lines) are shown.

Figure 4

Expected importation risks of infectious travellers under quarantine control strategies and increasing traveller volumes. Prevalence of COVID-19 among inbound travellers between April to July 2020 that were estimated from actual data were used in the simulations. (A) The number of infectious travellers who were released to the community. Median (thick horizontal tick), interquartile (shaded rectangles) and 95% quantiles (solid vertical lines) of 2,000 simulations were shown, while grey dashed line indicates the number of 1. (B) The risk of releasing at least one infectious traveller to the community. Mean (dots) and 95% CI (vertical lines) are shown.

Figure 5

Expected number of infectious travellers that were released to community (A) and risk of released at least one infectious traveller to the community (B) when one on-arrival test positive in different traveller volumes. In panel A, median (thick horizontal tick), interquartile (shaded rectangles) and 95% quantiles (solid vertical lines) of 2,000 simulations were shown, while grey dashed line indicates the number of. In panel B, mean (dots) and 95% CI (vertical lines) of the probability are shown.