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Record W3183958982 · doi:10.1016/j.lanwpc.2021.100209

Restarting international travel will be messy but enabled by improved risk-based analyses

2021· article· en· W3183958982 on OpenAlexafffund
Kelley Lee

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsRisk analysis (engineering)BusinessComputer science

Abstract

fetched live from OpenAlex

There is now abundant evidence that international travel has been a major driver of the COVID-19 pandemic and its prolonged duration [1Russell T Wu J Clifford S Edmunds WJ Kucharski A Jit M. Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study.Lancet Public Health. 2020; (December 7)https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30263-2/fulltextGoogle Scholar, 2Sigler T Mahmuda S Kimpton A. et al.The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.Globalization and Health. 2021; 17 (Article 56)https://doi.org/10.1186/s12992-021-00707-2Crossref PubMed Scopus (46) Google Scholar]. Travel restrictions adopted, in an attempt to stem the spread of SARS-CoV-2, have impacted billions of lives and the functioning of whole societies. Alongside cancelled events large and small, disruptions to trade [[3]Söderlund B. The impact of travel restrictions on trade during the COVID-19 pandemic. VoxEU, 4 November 2020Google Scholar] and shuttered businesses, millions have become stranded or separated from family, friends and work [[4]OECDManaging international migration under COVID-19. OECD Policy Responses to Coronavirus (COVID-19), Paris10 June 2020Google Scholar]. The UNWTO describes 2020 as “the worst year on record for tourism,” with an estimated US$1.3 trillion loss in international expenditures [[5]United Nations. How COVID-19 is changing the world: a statistical perspective, Volume III. New York, 2021. https://unstats.un.org/unsd/ccsa/documents/covid19-report-ccsa_vol3.pdfGoogle Scholar]. It is little wonder that easing travel restrictions, or at least applying them in more discerning ways, has become an urgent policy priority. Multiple systems have already been agreed, or are under negotiation, for easing restrictions based on immunization status [[6]McGregor G. Vaccine passports are here – so why are there still so many travel restrictions?. Fortune, 9 June 2021Google Scholar]. For some high-income countries rolling out vaccines at speed, along with countries keen to revive tourist revenues, overcoming the logistical challenges of creating a secure, interoperable certification system has become a major focus. The introduction of so-called vaccine passports will create categories of travellers entering a jurisdiction and determine the testing and quarantine requirements they will be subject to. The airline industry, in particular, is pressing governments to implement these systems ahead of the summer holiday season in the northern hemisphere, describing the resumption of high volumes of travel as “absolutely critical” [[7]Gilbertson D. Absolutely critical to both countries’: US, UK airlines urge lifting of travel restrictions.USA Today. 7 June 2021; https://www.usatoday.com/story/travel/airline-news/2021/06/07/covid-travel-restrictions-airlines-can-us-citizens-travel-to-uk/7584332002/Google Scholar]. Amid this intense pressure, however, what has become clear is that there remains a lack of agreed methodology to assess travel-related public health risks from COVID-19. When the World Health Organization (WHO) recommended against travel restrictions on 30 January 2020, upon declaring a public health emergency of international concern, the Director-General reminded States Parties that any “additional health measures” needed to be supported by scientific principles and available evidence [[8]WHO. WHO Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). Geneva, 30 January 2020. https://www.who.int/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov)Google Scholar]. It soon became apparent that available evidence from previous outbreaks was not helpful for informing the control of SARS-CoV-2 in a globally interconnected world. When States Parties proceeded near universally to adopt a host of travel restrictions, and then keep them in place for an unprecedented duration, what filled this evidence vacuum was a blend of precautionary principle, evolving science, economic lobbying and political ideology. In this context, Benjamin Cowling and colleagues [[9]Yang B., Tsang, T, Wong, J., He Y. et al. The differential importation risks of COVID-19 from inbound travellers and the feasibility of targeted travel controls: A case study in Hong Kong. 10.1016/j.lanwpc.2021.100184Google Scholar] provide a timely methodological contribution. Their study seeks to determine whether it is possible to assess the transmission risk of international arrivals, based on source country, as the basis of screening travellers for admission. Much of the evidence describing the role of travel in SARS-CoV-2 importation, demonstrated through genomic sequencing data, has been retrospective [[10]da Silva Filipe A. Shepherd J.G. Williams T. et al.Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland.Europe into Scotland. Nature Microbiology. 2021; 6 (2021): 112-122https://doi.org/10.1038/s41564-020-00838-zCrossref PubMed Scopus (55) Google Scholar]. The limited availability of methodologies to inform real time decision making, bringing together epidemiology, observational studies and other evidence to enable risk assessment, leaves border management reliant on less discriminate measures such as travel bans by citizenship or perceived “hot spots”. While the methodology put forth by Yang et al. may soon be overtaken by the implementation of immunization-based systems, until much of the world is fully vaccinated, better methods of risk assessment remain helpful. In December 2020, WHO issued guidelines to support the development of a risk-based approach to border management to inform mitigation measures [[11]WHO. Risk assessment tool to inform mitigation measures for international travel in the context of COVID-19. Geneva, 16 December 2020. https://www.who.int/publications/i/item/WHO-2019-nCoV-Risk-based_international_travel-Assessment_tool-2020.1Google Scholar]. Whether variables such as case incidence per 100,000 population over time, median incubation period and travel volumes prove the right ones to assess travel-related risks during COVID-19, the important advance is recognition by WHO of the need to shift from blanket recommendations to algorithms that enable real time, context specific decision making. The International Health Regulations Review Committee, in its final report in April 2021, affirmed the need to support the development of risk-based approaches [[12]WHO. Report of the Review Committee on the Functioning of the International Health Regulations (2005) during the COVID-19 response. Geneva, 30 April 2021. https://www.who.int/publications/m/item/a74-9-who-s-work-in-health-emergenciesGoogle Scholar]. Importantly, strengthening risk assessment should be seen as an enabler, not a hindrance, to the easing of travel restrictions. Risk assessment will help identify the appropriate testing and quarantine protocols for fully vaccinated, partially vaccinated, unvaccinated, and previously infected travellers. These protocols will be directly informed by the evolving science on the efficacy of different vaccines (especially against known and emerging variants of concern), the transmissibility and virulence of these variants, and the occurrence of breakthrough infections. SARS-CoV-2 surveillance data, alongside vaccination rates by jurisdiction, will also be key data points. Risk assessment brings this data together with consideration of the social and economic impacts of travel restrictions. But it is not led by these considerations. Reopening travel in Europe during the summer months of 2020, driven by economic interests, undid much of the hard work of suppressing the virus through repeated lockdowns [[13]Mallon P. Crispie F Gonzalez G. et al.Whole-genome sequencing of SARS-CoV-2 in the Republic of Ireland during waves 1 and 2 of the pandemic. MedRxiv (pre-print), 10 February 2021Crossref Scopus (0) Google Scholar]. The reality is that no travel during this pandemic has been risk free and future travel will entail risk. The key challenge is to appropriately assess and manage those risks in ways that enable travel to resume in a sustainable way. To do so, without appropriate risk assessment, would be like a pilot flying blindly without navigation. The author declares no competing interest. The differential importation risks of COVID-19 from inbound travellers and the feasibility of targeted travel controls: A case study in Hong KongModerate 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. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.258
GPT teacher head0.361
Teacher spread0.102 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2021
Admission routes2
Has abstractyes

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