Hong Kong Med J 2020 Jun;26(3):255–7 | Epub 3 Jun 2020
Hong Kong Academy of Medicine. CC BY-NC-ND 4.0
COMMENTARY
Travel restrictions in the rising COVID-19
pandemic
William Xue, Cynthia Lam, HH Yeung, CS Wong, Venus LY Chan, YS Wong
Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
Corresponding author: Mr William Xue (wxue@link.cuhk.edu.hk)
![](https://www.hkmj.org/sites/default/files/mjimage/pdficon.png)
The first reported case of coronavirus disease
2019 (COVID-19) dates to November 2019 at
Wuhan, China. It has since spread domestically
and internationally, with cases exported via land,
sea, and air travel. On 25 January 2020, the Hong
Kong government raised the response level under
the “Preparedness and Response Plan for Novel
Infectious Disease of Public Health Significance”
to Emergency Response Level.1 This response level
implies a high and imminent threat to the health of
the local population, and public health measures
were initiated accordingly.2 Initial measures
included case isolation, quarantine, and medical
surveillance of close contacts.3 From 3 February,
all border crossings to and from Hong Kong were
closed, except for Hong Kong International Airport,
Shenzhen Bay Bridge, and Hong Kong–Zhuhai-Macao Bridge.4 Many from the general public and the
healthcare industry demanded further actions as the
Hospital Authority Employees Alliance organised
a strike to pressure the government into ordering
a total border shutdown.5 On 25 March, extensive
immigration restrictions were implemented in view
of the spreading global pandemic. Non-residents
arriving in Hong Kong by air were denied entry, and
all travellers coming from mainland China, Macao,
or Taiwan were subject to a 14-day compulsory home
quarantine.6 These measures are in accordance with
Article 18 of the International Health Regulations,7 in
which the World Health Organization recommends
“restrictions on persons from affected areas” as a
potential means to reduce the international spread
of disease, while avoiding unnecessary interference
with international traffic.
The implementation of public health measures
is dependent on local context, balancing risks and
benefits. Nonetheless, our knowledge on the ongoing
pandemic is inevitably limited. The reproduction
number R0 of COVID-19 is under debate, with the
World Health Organization’s preliminary estimate of
1.4 to 2.58 challenged by Liu et al,9 who calculated
an average of 3.28. The efficacy of closing borders
compared with inaction or other forms of travel
restrictions in the containment of respiratory
zoonotic viral infections with documented person-to-person transmission has been well discussed in the literature.
Travel restrictions typically have a limited
effect in rapidly containing infections within a
defined geographical area.10 When implemented
extensively (>90%), these restrictions can delay
the national spread, global spread, and peak of an
epidemic, but effects on mitigating the magnitude
of pandemics are limited. The spread is negatively
correlated with the strain’s transmissibility, while
favourable factors include timely initiation, a longer
period of implementation, and certain geographical
sources.10 Other public health measures are
typically recommended, when considering their
implementation in isolation, such as providing
information to travellers, health monitoring,
improving community hygiene, and school or work
closures.11 12
Although extensive travel restrictions pose
resource burdens, they provide additional value in
reducing attack rates when combined with other
interventions. Cooper et al13 found that, although
suspension of 99.9% of air travel would only be able
to delay the spread of the disease to an individual
nation for ?4 months, combining this with other
local strategies, such as stocking up antivirals and
case quarantine, would allow a ?10-month delay of
pandemic spread and reduction of transmission of
40%. Meanwhile, Chong and Zee14 compared the
effect of travel restrictions coupled with antiviral
use and hospitalisation, showing that despite a <10%
mitigation of epidemic magnitude by individual
measures, together they bring synergy and result
in a 6-week delay. Therefore, the efficacy of travel
restrictions should be considered in combination
instead of in isolation.
There is heterogeneity in outcome measures
between reviews. For example, Mateus et al10 took
a quantitative approach, measuring the days of delay
in epidemic peaks, pandemic spread, or magnitude of
the spread; whereas Huizer et al11 took a qualitative
approach, allocating arbitrary scores in a framework
that comments on the efficacy and feasibility
of different measures. Thus, in the context of
COVID-19, “preventing or delaying the spread” or
“flattening the curve” should be placed at a higher
priority than “containing the virus”, so as to provide a buffer for institutions to better prepare for the
epidemic, for example by preparing isolation wards,
sourcing and stocking up on necessary personal
protective equipment for frontline healthcare
professionals, or developing and producing
treatments or vaccines.14
A major limitation of mathematical models
is that they cannot perfectly replicate real-life
scenarios. Simulations can also be subject to bias
in terms of study design and assumptions, as well
as the credibility of the input data. For example, a
probabilistic mathematical study on Pacific Island
Countries and Territories by Eichner et al15 assumed
that voluntary travel volume decreases as people
get increasingly sick. This might not be the case for
Hong Kong because the Hospital Authority treats all
patients with suspected COVID-19 free of charge,
which may attract an increase in cross-border
medical tourism. Eichner et al15 also assumed that
no other pandemic control measures would be used,
whereas a package of public health interventions
have been implemented in Hong Kong in response to
COVID-19.16 Finally, Eichner et al15 found missing and
suboptimal travel data from island nations, to such
an extent that data from different years had to be
used to extrapolate total annual traveller numbers. In
Chong and Zee’s clinical model study,14 although they
assumed that all identified cases accepted voluntary
quarantine, they did not specify their definitions of
90% and 99% travel restrictions. This could affect
the comparability of studies to the actual situation,
as closure of points of entry is a dynamic process
with travellers changing between different means
of entry as long as there is no complete closure of
borders. The lack of observational and experimental
studies also leads to difficulty for policymakers
to take reference. There have been proposals of
novel decision support tools that simulate real-life
situations more comprehensively.17 These tools, however, require further validation.
In consideration of the limitations of these
models, we recommend further research. First,
with increasing computational power, more
comprehensive mathematical models could be built
to simulate real-life situations more accurately, taking
into account the interactions between different
public health measures, as well as heterogeneous
mixing of population demographics. Second, there is
a need for an international framework outlining the
ways, timing, and magnitude of travel restrictions
according to the stage of an outbreak (recent outbreak
vs established pandemic). Finally, decision support
tools should be further evaluated and validated.
In conclusion, extensive travel restrictions
should be considered only in conjunction with other
public health measures in an emerging pandemic.
In isolation, such restrictions may not effectively
contain an epidemic, but they can delay its spread and thus provide a buffer for the design, resourcing,
and implementation of contingency plans. We hope
that this commentary aids our policymakers in
formulating public health decisions. We also urge the
general public to comply with isolation, quarantine,
social distancing, and proper personal hygiene
measures in order to help contain the COVID-19
pandemic.
Author contributions
Concept or design: W Xue.
Acquisition of data: All authors.
Analysis or interpretation of data: All authors.
Drafting of manuscript: W Xue.
Critical revision of the manuscript for important intellectual content: All authors.
Acquisition of data: All authors.
Analysis or interpretation of data: All authors.
Drafting of manuscript: W Xue.
Critical revision of the manuscript for important intellectual content: All authors.
All authors had full access to the data, contributed to the study, approved the final version for publication, and take
responsibility for its accuracy and integrity.
Conflicts of interest
All authors have disclosed no conflicts of interest.
Acknowledgement
We would like to thank Professor Jean-hee Kim from The
Jockey Club School of Public Health and Primary Care for
guiding us through the writing of this review. We would
also like to thank our classmate Mr Yiu-hei See for helpful
discussions leading to significant improvements in this
manuscript.
Funding/support
This commentary received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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