Effectiveness of travel restrictions in the rapid containment of human influenza: a systematic review

Ana LP Mateus a, Harmony E Otete b, Charles R Beck b, Gayle P Dolan c & Jonathan S Nguyen-Van-Tam b

a. Field Epidemiology Training Programme, Public Health England (East Midlands Office), Nottingham, England.

b. University of Nottingham Health Protection and Influenza Research Group, Clinical Sciences Building, Nottingham City Hospital, Hucknall Road, Nottingham, NG5 1PB, England.

c. Field Epidemiology Service, Public Health England (North East Office), Newcastle upon Tyne, England.

C. orrespondence to Jonathan S Nguyen-Van-Tam (email: jvt@nottingham.ac.uk).

(Submitted: 11 January 2014 – Revised version received: 02 September 2014 – Accepted: 03 September 2014 – Published online: 29 September 2014.)

Bulletin of the World Health Organization 2014;92:868-880D. doi: http://dx.doi.org/10.2471/BLT.14.135590

Introduction

Travel restrictions were included in the WHO interim protocol: rapid operations to contain the initial emergence of pandemic influenza that was published in 2007 by the World Health Organization (WHO).1 However, as they would hamper global travel and trade, such restrictions are not recommended by WHO once the global spread of pandemic influenza is established.2,3 In 2009, some countries applied travel restrictions as one of several strategies to prevent the introduction of the influenza virus A(H1N1)pdm09 into their territories but the effectiveness of this approach has subsequently been questioned.4 Research on influenza has focused on the evaluation of the effectiveness and impact of pharmaceutical interventions.5 As quantitative assessment of the effectiveness of travel restrictions in pandemic situations tends to be more challenging, there are scarce data on this topic. In any meta-analysis of surveillance data from multiple studies, it is difficult to quantify and compare the effectiveness of travel restrictions because such interventions are frequently implemented with other countermeasures and without following standardized protocols.6 However, mathematical models can be used to predict the effectiveness of each type of intervention and inform policy-makers at national and international levels. In 2009, a systematic review of studies based on such models revealed limited evidence of the effectiveness of restrictions in air travel – within and between countries – in the containment of pandemic influenza.7 There has been no more recent systematic assessment of the effectiveness of restrictions in land, sea or air travel as isolated interventions. We therefore decided to assess the effectiveness of travel restrictions in the rapid containment of influenza strains with pandemic potential, in a systematic review that incorporated data collected during the 2009 pandemic.

Methods

Before commencement, our protocol was registered with PROSPERO – the international prospective register of scientific reviews maintained by the United Kingdom of Great Britain and Northern Ireland’s National Institute for Health Research.8 We conducted a systematic review according to the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.9 We assessed the evidence for restrictions in internal travel – travel within the same country – or international travel – travel between two or more countries – affecting the spread of influenza. We considered the air, terrestrial or maritime transportation of humans to or within countries affected by seasonal or pandemic influenza. The outcome measures of interest were epidemiological characteristics and some viral transmission parameters of influenza such as the basic reproductive number (R 0 ). Studies eligible for inclusion were reports, reviews, meta-analyses, mathematical modelling studies and observational and experimental studies published before May 2014. Studies that only evaluated the spread of influenza in animals or animal products were excluded.

Search strategy

We searched numerous health-care databases and sources of grey literature (Box 1). Critical keywords and thesaurus heading terms were initially tailored to MEDLINE searches and then adapted for other sources as necessary. The full search construct was included in the registered protocol.10 We contacted field experts and undertook reference and citation tracking to identify further relevant literature.

Box 1. Sources of literature included in this systematic review Health-care databases CINAHL (Cumulative Index to Nursing and Allied Health Literature)

Cochrane Library – Central Register of Controlled Trials

EMBASE

PubMed – including MEDLINE

World Health Organization Global Index Medicus Evidence-based reviews Bandolier

Cochrane Library – Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment Database, NHS Economic Evaluation Database Guidelines United Kingdom Department of Health

United Kingdom National Institute for Health Care and Excellence – Evidence Search

United States Centers for Disease Control and Prevention – Guidance Grey literature Consultation with domain experts – Martin Cetron (Centers for Disease Control and Prevention, Atlanta), John Edmunds (London School of Hygiene & Tropical Medicine, London), Peter Grove (Department of Health, London), Richard J Pitman (Oxford Outcomes, Oxford)

OpenSIGLE system for information on grey literature in Europe

United Kingdom National Institute for Health Care and Excellence – Evidence Search

Web of Science Manual searching of relevant journals Eurosurveillance

Emerging Infectious Diseases Reference tracking Reference lists of all studies selected for inclusion were searched to identify further relevant studies Citation tracking Web of Science – Science Citation Index

Google Scholar Internet searching www.google.com

www.dh.gov.uk

www.hpa.org.uk – now: www.phe.gov

www.who.int

www.cdc.gov

www.flu.gov

Study selection

All records identified were imported into the EndNote X6 software package (Thomson Reuters, San Francisco, United States of America). Following the removal of duplicates, all remaining records were screened for inclusion against the protocol’s eligibility criteria by two researchers.8 We used a three-stage sifting approach to review titles, abstracts and full texts. Where disagreements arose, a third reviewer provided arbitration.8

Data extraction

All records that met the eligibility criteria were subject to data extraction. Two reviewers independently extracted study data using a piloted form; any disagreements were resolved with a third reviewer. The full list of data items extracted is available on PROSPERO.8

Assessing risk of bias

Risk of bias was assessed at both study and outcome level. We used an evaluation tool developed by the United States Agency for Healthcare Research and Quality11 for assessing such risk in reviews. Since we are not aware of a previously validated instrument to assess risk of bias in mathematical modelling studies, we developed a tool based on the principles for the construction of mathematical models recommended by the London School of Hygiene & Tropical Medicine,12 in consultation with an experienced modeller8 (see Appendix A; available at: http://www.nottingham.ac.uk/research/groups/healthprotection/documents/supplementary-data-sr-travel-restrictions-influenza-mateus-et-al-220914.pdf).

Summary measures and data synthesis

Descriptive statistics were calculated using Excel 2010 (Microsoft, Richmond, USA). We used a recognized framework to synthesize the extracted data and assessments of risk of bias in a narrative style.13

Results

Study selection and characteristics

Before removal of duplicates, we identified 8836 potentially relevant records. However, only 23 studies – 19 mathematical modelling studies, one time-series analysis, two literature reviews and one systematic review – met our eligibility criteria (Fig. 1).4,7,14–34

Fig. 1. Flowchart for the selection of studies on the effectiveness of travel restriction in the containment of human influenza

CDC: United States Centers for Disease Control and Prevention; CINAHL: Cumulative Index to Nursing and Allied Health Literature; DH: United Kingdom Department of Health; NHS: United Kingdom National Health Service; WHO: World Health Organization.

Of the modelling studies included, 14 used stochastic models,4,15,16,22,23,25–29,31–34 two used deterministic models,18,19 two used a combination of both stochastic and deterministic methods14,17 and one used a Poisson regression model.24 Six studies15–19,31 were based on meta-population models of influenza spread35 and one4 on an alternative model.36 The focus of the included studies was the effectiveness of internal22,23,26,27,29 or international4,14–19,24,25,31–34 travel restrictions or combined internal and international travel restrictions.28,30 All but three of our included studies involved assessments of the impact of restrictions on air travel.22,25,26 Only one assessed the impact of restrictions on aerial, maritime and terrestrial transportation.34 The characteristics of the included modelling studies and time-series analysis are presented in Appendix A.

The systematic review that we included synthesized evidence from modelling studies published between 1990 and September 2009.7 The literature reviews that we included evaluated evidence from mathematical modelling studies on the containment of pandemic influenza and evidence used for preparedness planning in the United Kingdom.20,21

Risk of bias within studies

Of the 20 studies based on mathematical modelling or time-series analysis, 17 were found to be at low risk of bias (Table 1). The other three were found to be at moderate risk of bias –because of limitations in the study design22,24 or the low quality of travel data.25 Methodological issues that may have led to bias included a lack of transmission variation during the progression of epidemics, seasonality, heterogeneous mixing and varying susceptibility of populations.14,26,27,29,34

The systematic and literature reviews were at moderate risk of bias (Table 2). The systematic review7 was based on literature from only one health-care database and on a snow-balling strategy that could have introduced selection bias. Neither of the literature reviews included any assessment of the design and quality of the studies that were included or detailed descriptions of the eligibility criteria applied.20,21

Synthesis of results

Internal travel restrictions

Travel restrictions appeared to have limited effectiveness in the containment of influenza at local level (Table 3 and Table 4; Table 3 is available at: http://www.who.int/bulletin/volumes/92/12/14-135590).

With pandemic influenza A(H1N1)pdm09 in Mongolia, the estimated delay of the pandemic peak varied between 1.0 and 1.5 weeks when 50% road and rail travel restrictions over 2–4 weeks were simulated.26 The corresponding impact on the attack rate was minimal – e.g. 95% travel restrictions led to a reduction of just 0.1%.26 A study set in the USA revealed similar findings – e.g. a delay in spread of 2–3 weeks if travel restrictions were 99% effective and implemented in conjunction with border restrictions that prevented the entry of infected travellers.28 Travel restrictions alone could delay spread by 1 week but only if implemented within 2 weeks of the first case.28 In one simulation, border controls preventing 99.9% of cases entering any given country delayed epidemic spread by up to 35 days.24 Another study in the USA presented analogous results – e.g. a 90% restriction on long-distance flights led to delays in the epidemic peak that ranged between a few days and a few weeks.27 Effectiveness of travel restrictions decreased as the transmissibility of the strain increased; travel restrictions reduced the incidence of new cases by less than 3%.27 According to a time-series analysis in the USA, a 50% restriction in air travel during the 2001–2002 influenza season would have delayed the peak mortality associated with novel strains of seasonal influenza by 16 days – i.e. compared with the timing of the peak in previous years.30

Internal travel restrictions in England, Scotland and Wales in the United Kingdom were predicted to have minimal impact on the magnitude of the peak and in delaying the spread of the epidemic – possibly because there are some densely populated urban areas and relatively high levels of population movement.28 However, in a recent review, it was estimated that a combination of internal and international travel restrictions could help to stagger the impact of a pandemic within a country such as the United Kingdom, by desynchronizing localized outbreaks.21 In Australia, it was reported that the impact of 80–99% restriction of air travel between major city hubs was less when varying transmissibility rather than constant transmissibility was simulated. 29 In the same investigation, effectiveness fell when strain transmissibility was increased.29 In the Republic of Korea, restriction of travel between cities by more than 50% reduced the epidemic peak by less than 0.01% when constant transmissibility was modelled.23 When variations in transmissibility were simulated, such travel had to be restricted by more than 90% for the epidemic peak to be delayed significantly.23 Travel restrictions would reduce the spread to new cities but could also increase the risk of large localized outbreaks.23 In China, it was observed that overall R 0 would increase if symptomatic travellers were banned from moving from areas with high prevalence of seasonal influenza to areas with low prevalence. When symptomatic travellers were banned from leaving low-prevalence areas, a decrease in overall R 0 to less than one was predicted.22

International travel restrictions

International travel restrictions also appeared to have limited effectiveness (Table 5 and Table 6; Table 6 is available at: http://www.who.int/bulletin/volumes/92/12/14-135590). Low-level restrictions – i.e. restrictions of less than 70% – were the least effective in containing the spread of epidemics between countries. It was found that a 40% restriction of air travel would only delay the spread of influenza A(H1N1)pdm09 from Mexico to other countries by less than 3 days.4 In a high transmissibility scenario, a 20% or even a 50% reduction in the volume of travellers would not have any significant impact on the global spread of influenza A(H5N1).15 In a meta-population model of pandemic influenza, based on the 1968–1969 influenza A(H3N2) pandemic virus it was predicted delays in the epidemic peak of 9 and 14 days with 50% and 90% restriction of air travel, respectively.18

In Italy, relatively large delays were reported in reaching an influenza A(H5N1) peak – i.e. 7–37 days, depending on the level of influenza transmissibility and the extent of the restrictions simulated.17 Travel restrictions had no beneficial effect on attack rate if the level of strain transmissibility was moderate or high.17 In a more recent review, it was estimated that introduction of pandemic influenza into the United Kingdom could be delayed by up to 2 months if there was an almost complete – e.g. 99.9% – ban on air travel.20 However, the size of the effect was considerably reduced, to just 1–2 weeks, if the level of restriction was lowered to 90%.20 Similar observations were made in an assessment of the impact of restrictions of air, land and sea travel on the introduction of H1N1 pdm09 into Hong Kong Special Administrative Region (SAR), China.34 In this study, it was estimated that restrictions of 90% and 99% on all modes of transportation would delay the epidemic peak by up to 6 and 12 weeks, respectively, when R 0 was set to 1.4.34 When R 0 was set to 1.7, a restriction of 99% on all modes of transportation would delay the epidemic peak by up to 8 weeks and halve the cumulative attack rate. Air travel restrictions appeared to be the most effective isolated intervention, even though most infected cases would probably enter Hong Kong SAR by land travel from mainland China.34 Although one review of the evidence from mathematical modelling concluded that air travel bans would probably have a similar effect irrespective of the pandemic’s country of origin,21 another report believed that the effectiveness of such restrictions would vary according to the geographical source of the pandemic.31 If air travel bans delayed the epidemic so that it coincided with the usual influenza season, the apparent number of cases and the size of the peak in the epidemic could both increase.31 However, the opposite trends might be observed if the travel restrictions coincided with a period of low strain transmissibility.31 By restricting air travel by 95%, it should be possible to delay pandemic spread across the USA – of an infection originating in Sydney or Hong Kong SAR – by 2–3 weeks.31 However, there was no corresponding impact if the geographical origin of the pandemic was London because of London’s high flight densities and interconnectivity.31 The selective cancellation of a quarter of all connection flights between 500 major cities worldwide could be more effective than the closure of all of the cities’ airports – reducing the number of infected travellers by an additional 19%.32 A review of air travel restrictions between Asia and the United Kingdom indicated that such restrictions would stop no more than 90% of infected travellers from the pandemic’s country of origin.21 If air travel from all affected countries was restricted by 90.0% and 99.9%, the pandemic wave would be delayed by 3–4 weeks and up to 4 months, respectively,21,28 but such intensive restrictions would clearly have negative social and economic impacts. A systematic review found that extensive air travel restrictions – e.g. restrictions of more than 90% – could delay the spread of pandemics by up to 4 months if the strains involved had low to moderate transmissibility.7 However, such restrictions appeared ineffective if the strains involved had high transmissibility – i.e. if R 0 was 2.4.7 In general, a combination of interventions appeared to be more effective than the implementation of travel restrictions in isolation.7

Discussion

The results of our systematic review indicate that overall travel restrictions have only limited effectiveness in the prevention of influenza spread, particularly in those high transmissibility scenarios in which R 0 is at least 1.9 (Box 2). The effect size varied according to the extent and timeliness of the restrictions, the size of the epidemic, strain transmissibility, the heterogeneity of the travel patterns, the geographical source and the urban density of international travel hubs. Only extensive travel restrictions – i.e. over 90% – had any meaningful effect on reducing the magnitude of epidemics. In isolation, travel restrictions might delay the spread and peak of pandemics by a few weeks or months but we found no evidence that they would contain influenza within a defined geographical area.

Box 2. Summary of findings of the 23 studies assessed Internal travel restrictions: general observations Have limited effectiveness

Delay pandemic spread by about 1 week

Delay pandemic peak by about 1.5 weeks

Have little impact on magnitude of pandemics – e.g. they may reduce attack rates by <>

Simulated impact is particularly weak in scenarios that involve strains with high transmissibility Internal travel restrictions: risk of bias assessment Relevant studies have low to moderate risk of bias

Paucity of data on terrestrial travel may have led to an overestimation of the impact of travel restrictions

Many simulations take no account of the characteristics of human populations – e.g. the mixing and variation of susceptibility across age groups – or of seasonality. Such limitations could well have affected the simulated spread of pandemic waves and impacts of interventions International travel restrictions: general observations Have limited effectiveness – e.g. 90% air travel restriction in all affected countries may delay spread of pandemics by 3–4 weeks

Have minimal impact on the magnitude of pandemics, typically reducing attack rates by less than 0.02%

May prolong the seasonal influenza season

May result in higher epidemic peak if resultant delay causes pandemic wave to coincide with seasonal influenza wave

Simulated impact particularly weak in scenarios that involve strains with high transmissibility

Extensive restriction of international air travel might delay introduction of a pandemic into a country by up to 2 months and delay pandemic spread by 3–4 months

Would not prevent introduction of a pandemic into any given country

May give time for other interventions – e.g. the production and distribution of effective vaccines and antiviral drugs

Social and economic impacts need to be evaluated International travel restrictions: specific measures May have benefits compared with more widespread restrictions – e.g. in one simulation, compared with the closure of all of the cities’ airports, the targeted reduction of a quarter of flight connections between 500 major cities gave a greater reduction in the number of infected travellers

Compared with banning air travel by adults, the banning of air travel by children may be more effective at delaying the spread of a pandemic but is socially impractical International travel restrictions: risk of bias assessment Relevant studies have low to moderate risk of bias

A paucity of data on travel by sea and land may have led to an overestimation of the impact of air travel restrictions on the containment of influenza pandemics

Much of the information available on air travel has a lack of detail on flight destinations and numbers of travellers and this may have led to inaccurate assumptions being made about the spread of influenza

Again, many simulations take no account of the characteristics of human populations – e.g. the mixing and variation of susceptibility across age groups – or of seasonality and such limitations could well have affected the simulated spread of pandemic waves and impacts of interventions

When simulating novel pandemic strains, validation of models was an issue; mathematical models need to be validated against surveillance data to improve their value as predictive tools for policy-makers

Several limitations associated with our review warrant discussion. We included mathematical modelling studies that simulated very diverse scenarios with varying levels of R 0 , geographical locations, means of transportation, strains and population characteristics. A paucity of surveillance data concerning the impact and effectiveness of nonpharmaceutical interventions meant that our observations had to be mainly based on simulations.6 While mathematical models are important tools that can be used to inform policy-makers, they cannot account fully for all aspects of real-life situations.

The lack of available data from observational or experimental studies precluded the conduct of the meta-analysis and sensitivity analysis that formed part of the protocol that we registered.8 Most of the studies that we included in our review used probabilistic models that appeared to have adequate levels of complexity to simulate disease spread and the impact of interventions. In comparison, deterministic models are less complex and do not take uncertainty into account but are still useful when limited data are available and a rapid simulation is needed.7 Most of the studies we reviewed were limited by a lack of consideration of heterogeneous mixing, socioeconomic status and the relationship between age and immunity.37 Many also simulated constant strain transmissibility during epidemics – even though transmissibility can vary over time because of seasonal climactic conditions, changes in host susceptibility and the effects of interventions such as social distancing, quarantine and the use of antiviral drugs.38 The authors of some of the articles noted concerns that may have affected model accuracy, such as issues with the quality of air travel data – e.g. a lack of flight itineraries28 – and the need to use crude estimates of the volume of travellers within and between countries. There was a general paucity of data on land and sea travel,25 although one of the studies provided comprehensive data on such travel.34 The tool we developed to assess the risk of bias in the mathematical modelling studies has not been validated and could have produced imprecise estimates.

The results of several studies indicate that, in reducing the global spread of influenza and the overall number of infected individuals, a combination of several different interventions is more effective than any single isolated measure.16,17,34 One study estimated that, when the strains involved have moderate transmissibility, a combination of antiviral prophylaxis, extensive travel restrictions and infant vaccination could reduce the cumulative attack rate by 77–87%.17 However, effective vaccines are not generally available at the point of emergence of a novel pandemic virus. The effectiveness of combined or single interventions can be affected by the timeliness of the implementation4,39 and this appears to be particularly relevant with strains of higher transmissibility.34

Often, in the context of pandemic preparedness and response, travel restrictions – especially at points of entry – have intuitive appeal to policy-makers because they demonstrate that a tangible attempt is being made to prevent the ingress of a novel virus or prevent onward spread. However, such an attempt is not always effective. WHO interim protocol: rapid operations to contain the initial emergence of pandemic influenza is implicitly focused on the creation of geographical cordons within a country and places more emphasis on the restriction of travel by land than on restrictions of air or sea travel.1 However, the relevant data that are available seem to indicate that restrictions on land travel would have a limited impact on containment or even on the slowing of transmission.34

It seems likely that, for delaying the spread and reducing the magnitude of an epidemic in a given geographical area,7 a combination of interventions would be more effective than isolated interventions.16,34 Travel restrictions per se would not be sufficient to achieve containment in a given geographical area, and their contribution to any policy of rapid containment is likely to be limited.

Competing interests

The University of Nottingham Health Protection and Influenza Research Group is currently in receipt of research funds from GlaxoSmithKline (GSK) and unrestricted educational grants for influenza research from F Hoffmann-La Roche and Astra Zeneca. However, this funding did not support any aspect of the present study. Prior to October 2010, JSNV-T received funding to attend influenza-related meetings and give lectures, and also consultancy fees and research funding from several manufacturers of antiviral drugs and influenza vaccines. JSNV-T was an employee of SmithKline Beecham, Roche Products and Aventis-Pasteur MSD prior to 2005 but now has no outstanding pecuniary interests by way of shareholdings, share options or accrued pension rights.

References