PLoS Med plos plosmed PLOS Medicine 1549-1277 1549-1676 Public Library of Science San Francisco, CA USA 10.1371/journal.pmed.1004283 PMEDICINE-D-22-04017 Research Article Medicine and health sciencesDiagnostic medicineVirus testing Medicine and health sciencesMedical conditionsInfectious diseasesRespiratory infections Medicine and health sciencesMedical conditionsRespiratory disordersRespiratory infections Medicine and health sciencesPulmonologyRespiratory disordersRespiratory infections Medicine and health sciencesMedical conditionsInfectious diseasesViral diseasesCOVID 19 Biology and life sciencesMolecular biologyMolecular biology techniquesArtificial gene amplification and extensionPolymerase chain reaction Research and analysis methodsMolecular biology techniquesArtificial gene amplification and extensionPolymerase chain reaction Medicine and health sciencesEpidemiology Biology and life sciencesOrganismsVirusesRNA virusesCoronavirusesSARS coronavirusSARS CoV 2 Biology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 Biology and life sciencesOrganismsVirusesViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 People and placesGeographical locationsEuropeEuropean UnionFrance People and placesGeographical locationsOceaniaFrench Polynesia Real-time surveillance of international SARS-CoV-2 prevalence using systematic traveller arrival screening: An observational study Real-time surveillance of international SARS-CoV-2 prevalence via systematic traveller arrival screening https://orcid.org/0000-0001-8814-9421 Kucharski Adam J. Conceptualization Formal analysis Investigation Methodology Writing – original draft 1 2 * https://orcid.org/0000-0003-3181-8326 Chung Kiyojiken Data curation Investigation Writing – review & editing 2 https://orcid.org/0000-0002-9913-5794 Aubry Maite Investigation Methodology Writing – review & editing 2 https://orcid.org/0000-0001-8921-1178 Teiti Iotefa Data curation Investigation Writing – review & editing 2 Teissier Anita Data curation Formal analysis Writing – review & editing 2 https://orcid.org/0000-0001-8132-0740 Richard Vaea Data curation Formal analysis Writing – review & editing 2 https://orcid.org/0000-0001-5610-6080 Russell Timothy W. Formal analysis Methodology Writing – review & editing 1 Bos Raphaëlle Data curation Formal analysis Writing – review & editing 3 Olivier Sophie Data curation Formal analysis Writing – review & editing 3 Cao-Lormeau Van-Mai Conceptualization Investigation Methodology Supervision Writing – original draft 2 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom Laboratory of Research on Emerging Viral Diseases, Institut Louis Malardé, Papeete, French Polynesia Clinical Laboratory, Institut Louis Malardé, Papeete, French Polynesia

The authors have declared that no competing interests exist.

 * E-mail: adam.kucharski@lshtm.ac.uk 8 9 2023 9 2023 20 9 e1004283 14 12 2022 22 8 2023 2023 Kucharski et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background

Effective Coronavirus Disease 2019 (COVID-19) response relies on good knowledge of population infection dynamics, but owing to under-ascertainment and delays in symptom-based reporting, obtaining reliable infection data has typically required large dedicated local population studies. Although many countries implemented Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing among travellers, it remains unclear how accurately arrival testing data can capture international patterns of infection, because those arrival testing data were rarely reported systematically, and predeparture testing was often in place as well, leading to nonrepresentative infection status among arrivals.

 Methods and findings

In French Polynesia, testing data were reported systematically with enforced predeparture testing type and timing, making it possible to adjust for nonrepresentative infection status among arrivals. Combining statistical models of polymerase chain reaction (PCR) positivity with data on international travel protocols, we reconstructed estimates of prevalence at departure using only testing data from arrivals. We then applied this estimation approach to the United States of America and France, using data from over 220,000 tests from travellers arriving into French Polynesia between July 2020 and March 2022. We estimated a peak infection prevalence at departure of 2.1% (95% credible interval: 1.7, 2.6%) in France and 1% (95% CrI: 0.63, 1.4%) in the USA in late 2020/early 2021, with prevalence of 4.6% (95% CrI: 3.9, 5.2%) and 4.3% (95% CrI: 3.6, 5%), respectively, estimated for the Omicron BA.1 waves in early 2022. We found that our infection estimates were a leading indicator of later reported case dynamics, as well as being consistent with subsequent observed changes in seroprevalence over time. We did not have linked data on traveller demography or unbiased domestic infection estimates (e.g., from random community infection surveys) in the USA and France. However, our methodology would allow for the incorporation of prior data from additional sources if available in future.

 Conclusions

As well as elucidating previously unmeasured infection dynamics in these countries, our analysis provides a proof-of-concept for scalable and accurate leading indicator of global infections during future pandemics.

Using travel testing data for SARS-CoV-2 infection obtained from international arrivals and departures, Adam J. Kucharski and colleagues estimate SARS-CoV-2 prevalence in multiple countries.

 Author summary Why was this study done?

• During the Coronavirus Disease 2019 (COVID-19) pandemic, the true dynamics of infections have been poorly understood globally.

• Although community infection surveys sampling individuals regardless of symptoms have provided crucial insights to inform policy in countries like the United Kingdom, expense and logistical challenges have prevented similar roll-out elsewhere.

 What did the researchers do and find?

• We identified an alternative source of routine information that can provide comparable insights on infection dynamics: Our analysis demonstrates that travel testing data among international arrivals can be used to reconstruct Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) prevalence in multiple countries.

• Applying our method to more than 222,000 arrival tests conducted in French Polynesia between July 2020 and March 2022, we estimated a peak infection prevalence at departure of around 2% in France and 1% in the USA in late 2020/early 2021, with a median prevalence of around 5% and 4%, respectively, estimated for the Omicron BA.1 waves in early 2022.

• We found that our infection estimates were a leading indicator of the later observed case dynamics in these countries and were consistent with subsequent observed changes in seroprevalence over time in France and the USA.

 What do these findings mean?

• Our results suggest that systematic collection of traveller testing data can enable real-time estimation of underlying epidemic dynamics in multiple countries.

• In our study, personal data about travellers—such as age and address—was not available for analysis. In future, linking traveller tests to demographic characteristics most relevant to infection status could enable a more detailed understanding of risk.

 http://dx.doi.org/10.13039/100010269 Wellcome Trust 206250/Z/17/Z https://orcid.org/0000-0001-8814-9421 Kucharski Adam J. http://dx.doi.org/10.13039/100018336 National Institute for Health Research Health Protection Research Unit NIHR200908 https://orcid.org/0000-0001-8814-9421 Kucharski Adam J. AJK was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant Number 206250/Z/17/Z) and the NIHR HPRU in Modelling and Health Economics, a partnership between PHE, Imperial College London and LSHTM (grant code NIHR200908). The views expressed are those of the authors and not necessarily those of the United Kingdom (UK) Department of Health and Social Care, the National Health Service, the National Institute for Health Research (NIHR), or Public Health England (PHE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS Publication Stage vor-update-to-uncorrected-proof Publication Update 2023-09-22 Data Availability Model code and data are available at: https://github.com/institutlouismalarde/covid-travel-testing. Outbreaks COVID-19 Introduction

Understanding the true extent of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection within a population is crucial for effective planning and response. As well as being a leading indicator of subsequent disease, it can help inform the timing and design of control measures both domestically and with respect to internal travel [1]. However, throughout the pandemic, insights into underlying infection dynamics have typically been limited, hindering countries’ ability to respond promptly and proportionately.

In the early stages of 2020, large undetected epidemics in several locations were first identified as a result of infected travellers from these areas being detected in other countries [2,3]. Limited testing availability also meant that infection dynamics had to be estimated from lagged indicators such as hospitalisations, with the number of infections derived from available data on severity [4]. Cross-sectional seroprevalence studies have since provided estimates of the extent of infection within populations, but with a lag and without information on when those infections occurred [5]. Moreover, the roll-out of vaccination and emergence of novel variants means estimation of infection dynamics based on severe outcomes or serological data is becoming more challenging [6]. In some instances, countries have tackled these issues by setting up routine community sampling regardless of symptoms, such as the ONS (Office for National Statistics) community infection survey and REACT-1 (Real-Time Assessment of Community Transmission) in the United Kingdom [7,8], as well as cohort studies tracking infection in specific workplaces, such as healthcare workers [9]. However, the expense and logistical complexity of such studies have resulted in limited deployment globally.

Despite a lack of studies tracking local infection prevalence, large numbers have been tested regardless of symptoms during the pandemic as a result of traveller screening programmes [10]. Countries have predominately used travel testing as a method of control, with positive individuals having to isolate, as well as being prevented from travelling at all if they test positive before departure. Algorithms have also been developed to predict individuals more likely to test at arrival based on demographic characteristics such as age and country of origin [11], which can be useful for detecting infections, but less relevant if the objectives to obtain unbiased estimates of overall population prevalence.

Although some countries with strict restrictions on traveller numbers have reported the number of arriving infections detected over time [12] and data have been reported from brief or small-scale testing programmes [13–15], there has been no systematic long-term data published from global travel testing. Tests have been conducted by different companies and agencies, and in combinations that can include both departure and arrival testing, typically without collation of these tests in consistent databases.

Despite the inconsistent way in which travel testing has generally been reported during the pandemic, such screening presents a unique opportunity for real-time surveillance of infection in multiple countries. Local community infection studies, or analysis of departure testing results, can only provide information on infections within the country conducting the study. In contrast, arrival testing can provide insights into infections among travellers from a range of different locations. However, there are challenges to interpreting arrival test results. If testing is implemented at both departure and arrival, then infections detected among arriving travellers only reflect a subset of all the infected individuals who attempted to travel.

Using data on how test positivity varies over the course of infection, and details of departure and arrival protocols, we developed a model of the travel screening process, and hence used arrival prevalence to reconstruct how many travellers would have tested positive in countries of departure. Incorporating data from French Polynesia, which had systematic arrival screening for SARS-CoV-2, we then test the potential to use local surveillance to recover underlying international infection dynamics.

 Methods Ethics statement

Communication of data from the surveillance system was approved by Comité d’Ethique de la Polynésie française (ref Avis n°90 CEPF 15_06_2021). Secondary data analysis was approved by the London School of Hygiene & Tropical Medicine Observational Research Ethics Committee (ref 28129).

 Data

Travel testing in French Polynesia was conducted in 2 distinct phases, with some additional adjustments to protocols during each phase. The first phase, between 15th July 2020 and 30th April 2021, was the “COV-CHECK” system, with travellers required to perform a polymerase chain reaction (PCR) test less than 72 h before departure as well as a self-test 4 days after arrival in French Polynesia [16]. Mandatory quarantine was added to this protocol on 20th February 2021. A schematic of the process is shown in Fig 1A.

10.1371/journal.pmed.1004283.g001 Impact of departure and arrival testing protocols on PCR prevalence.

(A) Possible outcomes for infected travellers in a scenario with predeparture and post-arrival testing. (B) Probability an individual will test PCR positive at different points since infection, based on self-tested asymptomatically tested participants [18]. Line shows median, with shaded region showing 95% CrI in this Bayesian analysis. (C) Probability infected traveller will be detected by a predeparture test, in scenario where test conducted 2 days before departure (dashed line). (D) Probability infected traveller will be detected by a post-arrival test conducted 4 days after arrival (dashed line), assuming no local acquisition of infection. (E) Illustrative epidemic showing proportion of population newly infected per day with 2 different variants. (F) Larger measured prevalence in predeparture testing corresponding to incidence curves in (E), based on positivity probability in (C). (G) Measured prevalence in post-arrival testing, corresponding to incidence curves in (E), based on positivity probability in (D). Grey line shows cumulative prevalence. CrI; credible interval; PCR, polymerase chain reaction.

The second phase started on 1st May 2021 with the option of taking either an antigen test within 48 h of departure or a PCR test within 72 h. Moreover, testing was performed on the day of arrival in French Polynesia [17] by nurses until 12th August 2021, then using the self-test COV-CHECK protocol until 27th December 2021, and finally reverted to nurses until the end of the surveillance period. Unvaccinated individuals (mostly children) had additional self-tests on day 4 and day 8 until 19th January 2022, then on days 2 and 5. As these repeat tests did not systematically have accompanying dates in the dataset, and hence, it was not possible to identify which test was conducted on day 0, we omitted these repeated test results (7,809 of 112,945 total test results that had a recorded country of origin) from the analysis. After 28th December 2021, departing travellers could take either an antigen test within 24 h of travel or a PCR test within 72 h. Throughout the pandemic, all arrival PCR tests were screened for mutations associated with novel variants and subset were sequenced to identify the specific imported variants, with sequencing activity typically focused in the early stages of each wave to detect initial arriving infections.

To protect privacy, age, nationality, and address of tested passengers were not available for analysis. The origin of tested passengers was therefore defined based on the flight number. Between 15th July 2020 and 17th November 2021, passengers on flights from Paris, France had a direct transit (via Canada or Guadeloupe) so it was not possible to leave or join the plane in transit. Later, this transit was via the United States of America (Los Angeles), where it was possible to leave or join the plane mid-journey. Between July 2020 and March 2022, there were 464,728 incoming travellers: 229,744 tourists and 234,984 returning residents [18]. We do not know whether passengers that took their plane in Paris or in Los Angeles came originally from another country, but because 90% of tourist arrivals in 2021 were reported as from these 2 original countries on arrival forms, we made the assumption that infected tourists or returning residents reflect infections acquired in the USA and France.

The primary public health aims of travelling testing in French Polynesia were 2-fold: (1) to limit virus dissemination (travellers found positive for SARS-CoV-2 were asked to self-isolate and were not allowed to travel to outer islands); and (2) to obtain early information on the introduction of new variants circulating in other countries and to be prepared for local circulation. The cost of traveller surveillance in French Polynesia was initially covered by the local government (until July 2021), then by the travellers themselves when registering on the (mandatory) travellers identification platform.

 Estimation of departure prevalence from arrival testing

If testing is implemented at both departure and arrival, then infections detected among arriving travellers only reflect a subset of all the infected individuals who attempted to travel (Fig 1A). Analysis of repeated PCR self-testing in a healthcare worker cohort [19] found that individuals can test positive for several weeks, with a peak in detection around 5 days post infection (Fig 1B). Departure testing is therefore most likely to prevent the most detectable infections from travelling (Fig 1C), which means if arrival testing is conducted shortly after arrival, the proportion of infections detected will be distributed differently to the departure tests (Fig 1D). For a given incidence of new infections in a country of departure, measured prevalence among departing and arriving travellers could therefore be considerably different, with overall arrival prevalence masking underlying dynamics in countries of departure (Fig 1E–1G).

We can estimate departure prevalence from arrival testing data as follows. If N individuals are tested at arrival, then the number who test positive is binomially distributed with probability q, where q = P(test positive at arrival | tested negative at departure) = P(arrival+ | departure–).

We can rewrite this probability in terms of arrival and departure tests: P(arrival+|departure–)=P(arrival+anddeparture–)/P(departure–).

If w is the probability an individual is infected at departure (i.e., could in theory test positive), then: P(arrival+anddeparture–)=wP(arrival+anddeparture–|infected) P(departure–)=wP(departure–|infected)+(1–w).

Details of how these probabilities can be calculated are provided in S1 Text.

Combining the above probabilities gives the following expression for the probability an individual tests positive at arrival: q=wP(arrival+anddeparture–|infected)wP(departure–|infected)+(1–w).

If we have observations on the number tested at arrival, and the number tested, we can therefore estimate the distribution of w, and hence, prevalence at departure (full details in S1 Text). Model code and data are available at: https://github.com/institutlouismalarde/covid-travel-testing.

 Pooling test model

For a pool of size n, the probability that there is at least 1 positive in the pool given true prevalence x is equal to p = 1-(1–x)n. For N pools, the likelihood of x is therefore given by the binomial distribution B(N,p). For each scenario, we calculated the maximum likelihood estimate for x and corresponding confidence interval generated from the profile likelihood.

 Results

The effectiveness of traveller screening based on biological outcomes, such as symptoms or test positivity, will depend on how long ago travellers were infected. In turn, this will depend on the dynamics of the epidemic in countries of departure [20]. During a growing epidemic, individuals are more likely to have been infected recently, whereas infections will generally be older in a declining epidemic. In situations where test positivity is influenced by the phase of the epidemic, it is often necessary to reconstruct infection dynamics using computationally intensive latent processes models [21]. However, we found that the presence of departure testing changes the distribution of infection timings among arrivals, as the individuals most likely to be detectable are least likely to travel (Fig 2A–2C). As a result, most arriving infections will have been infected within a narrower window that those detected at departure. This reduced the impact of epidemic phase on arrival positivity and allowed for reconstruction of departure prevalence from arrival data with a single multiplier calculated from PCR positivity curves and the specific departure and arrival testing protocol. Our approach could therefore generate accurate estimates for simulated departure prevalence dynamics, particularly if there was a gap of several days between the departure and arrival test, which reduced the variance of the time-since-infection for arrivals (Fig 2D–2F). In a sensitivity analysis, we found that estimates were more reliable with larger sample sizes and less biased when the testing process was accurately quantified, with correct assumptions about delay between tests and test sensitivity (S1 Fig).

10.1371/journal.pmed.1004283.g002 Observation and estimation of infection prevalence under different epidemic dynamics.

(A) Proportion of infected travellers detected relative to infection time, in a scenario with incidence declining 10% per day, and testing 2 days before departure, with 1 day in transit, and 4 days after arrival (dashed lines). Light green, travellers detected predeparture; dark green, travellers detected post-arrival; red, travellers missed. (B) Scenario in a stable epidemic, i.e., 0% daily change in incidence. (C) Scenario in epidemic with 10% daily growth. (D) Reconstruction of simulated epidemics from arrival testing data, assuming 5,000 arrivals tested per week. Solid points, “true” prevalence at departure with lines showing 95% binomial confidence interval; squares, measured prevalence at arrival in simulated scenario with a test 2 days before departure with 1 day in transit and another test 4 days post-arrival, with circles showing reconstructed departure prevalence from these data. (E, F) Reconstruction as described in (D) under different assumed epidemic dynamics.

To show how this approach can be used mid-pandemic, we analysed traveller data collected as part of the Coronavirus Disease 2019 (COVID-19) response in French Polynesia [16]. Between July 2020 and March 2022, more than 222,000 traveller tests were conducted as part of an arrival screening programme (Fig 3A). During the first phase of the surveillance strategy, travellers performed a self-test (COV-CHECK protocol) 4 days after arrival as well as a PCR test within 72 h of departure. Faced with novel variants, mandatory quarantine was added to the protocol in February 2021. Between May 2021 and March 2022, testing was performed on the day of arrival first by nurses until 12th August 2021, then using the self-test COV-CHECK protocol until 27th December 2021, and again by nurses until the end of the surveillance period. During 2021, the option was also added for an antigen test within 48 h of departure rather than a PCR test within 72 h.

10.1371/journal.pmed.1004283.g003 Arrival testing in French Polynesia, July 2020 to March 2022.

(A) Testing data and changes in main protocols over time. Initially travellers were tested 4 days post-arrival (d4), with additional quarantine introduced on 20th February 2021; later vaccinated travellers were tested on day of arrival (d0), with additional testing on day 4 and 8 for non-vaccinated individuals. Black line, number of tests performed; orange line, number of positive tests. (B) Percent of tests that were positive over time, with lines showing binomial confidence interval and blue line showing GAM fit with shaded 95% CI. (C) Local COVID-19 cases reported in French Polynesia, with arrows showing first detection of different variants among travellers. (D) Distribution of Ct values among positive arrivals into French Polynesia (orange bars) and routinely tested UK HCWs (cyan bars). (E) Estimated percent of infections that would be detected early on (i.e., within 5 or 10 days of infection) under different travel testing protocols using PCR and antigen tests in a growing epidemic. (F) Estimated percent of infections detected in a declining epidemic. COVID-19, Coronavirus Disease 2019; Ct, cycle threshold; GAM, generalised additive model; HCW, healthcare worker; PCR, polymerase chain reaction.

There were 1,341 positive arrival tests in the dataset analysed, with considerable variation in weekly prevalence over the course of the pandemic (Fig 3B). There were 3 main COVID-19 waves in French Polynesia—caused by wild type, Delta and Omicron—with Alpha detected among travellers without leading to widespread local transmission (Fig 3C). Among arriving travellers, 15% of measured cycle threshold (Ct) values were below 20 (Fig 3D), with a distribution that was lower than has previously been observed in self-testing of healthcare workers [19]. This is to be expected given travellers will typically be earlier in their infection at the point of the arrival test, as noted above (Fig 2). Such a pattern is also consistent with previous reports of shedding being higher than usual among positive arriving travellers [22]. Based on protocols implemented, we estimated that arrival PCR testing at day 4 would have been expected to detect around 60% of infected individuals within the first 10 days of their infection if an epidemic in the departure location was growing at 10% per day (Fig 3E), and around 40% during an epidemic that was declining 10% per day (Fig 3F). This illustrates the value of delayed arrival testing for detecting infections during rising SARS-CoV-2 transmission, as well as for reconstructing departure prevalence. However, given the percentages undetected, travel testing and quarantine would still need to be combined with other measures, such as a reduction in traveller numbers or domestic control measures, to prevent sustained transmission. In reality, the proportion of infections detected would be binomially distributed, and hence also subject to randomness, as well as from any uncertainty in the test sensitivity at departure and arrival.

The majority of tested arrivals into French Polynesia had either the USA or metropolitan France (with a few hours transit via the USA or Canada or Guadeloupe) as the origin for their trip (Fig 4A and 4B). This was consistent with reported residency data on immigration forms, with 90% of tourist arrivals in 2021 from these 2 countries [18]. We found that estimate prevalence at departure among travellers from the USA or France (Fig 4C and 4D), based on French Polynesia arrival tests, anticipated observed case dynamics in the 2 countries (Fig 4E and 4F), showing the value of traveller testing as a leading indicator. Peaks in prevalence occurred shortly before peaks in reported cases, as would be expected due to delays in symptom onset, symptomatic testing, and reporting in the country of origin. Adjusting for traveller testing protocols as outlined in Fig 1, we estimated a peak infection prevalence at departure of 2.1% (95% credible interval (CrI): 1.7, 2.6%) in France and 1% (95% CrI: 0.63, 1.4%) in the USA in late 2020/early 2021, with prevalence of 4.6% (95% CrI: 3.9, 5.2%) and 4.3% (95% CrI: 3.6, 5%), respectively, estimated for the Omicron BA.1 waves in early 2022.

10.1371/journal.pmed.1004283.g004 Reconstruction of infection dynamics in France and USA from arrival testing data in French Polynesia.

(A) Number of arrivals from France tested per week. Orange, tests performed at day 4 after arrival; yellow, tests performed at day of arrival. (B) Arrival testing from USA. (C) Estimated prevalence among arrivals from France, with maximum a posteriori estimate shown by black dots with lines showing Bayesian 95% high posterior density interval; blue line and shaded region, GAM fit to these data and 95% prediction interval. (D) Estimated prevalence among arrivals from the USA. (E) Domestic cases reported in France. (F) Domestic cases in USA, shown by black line, alongside SARS-CoV-2 concentration in wastewater [27], shown by orange line. (G) Comparison of estimated cumulative infections and observed seroprevalence in France. Black dots, observed national seroprevalence in France in October 2020 and February 2021 [28]; black triangle, observed national seroprevalence in November 2020 [29]; black square, estimated proportion infected by January 2021 [30]; blue line, cumulative incidence derived from blue line in (E), shifted to match initial value of black line; shaded region, bootstrap 95% prediction interval; grey line, cumulative per capita domestic cases reported. (H) Estimated cumulative infections and observed seroprevalence in USA. Black dots and lines, observed seroprevalence over time with 95% confidence interval [5]; red lines, estimated cumulative incidence over same periods, shifted to match initial values. GAM, generalised additive model; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

To further assess the ability of arrival screening to estimate international SARS-CoV-2 dynamics, we converted prevalence at departure (i.e., current positivity) into an estimate of incidence (i.e., new daily infections) and compared cumulative incidence over the same period as repeated serological surveys in France and the USA. In France, our estimate of cumulative incidence in late 2020 was slightly larger than implied in serological studies (Fig 4G). There are several potential explanations for this discordance. The volume of travel from France declined substantially during that wave, which may have affected representativeness of travellers, as risk-averse individuals could have been less likely to travel, and risk-taking individuals more likely to be infected in transit. As testing was conducted on day 4 during this period and there was a COVID-19 wave in French Polynesia in late 2020, there is also the possibility of infection post-arrival. In contrast, we found that our estimates for the USA closely matched increases in seroprevalence during epidemic waves in both late 2020 and late 2021 (Fig 4H).

Because systematic individual-level testing is resource intensive, we also explored the potential for pooled testing to generate estimates of prevalence at arrival. For example, travellers from a given airport of origin could submit a swab via a pooled collection system, with testing returning either a positive or negative result for each pool. We estimated that for a sample of 100 incoming travellers, there was considerable uncertainty in prevalence estimates when larger pool sizes were used (Fig 5A). However, this uncertainty was reduced for larger traveller volumes (Fig 5B). Surveillance design (e.g., pool size and whether aggregated daily or weekly) could therefore be tailored to ensure that prevalence estimates for a given pathogen provide the desired level of precision and/or required degree of confidence that prevalence is below a certain value (Fig 5C and 5D).

10.1371/journal.pmed.1004283.g005 Estimation of prevalence based on pooled testing.

(A) Scenario where 100 passengers are tested, either in pools of size 5 or 20, with the proportion of positive pools used to estimate prevalence. Dots show mean estimate, with 95% confidence interval shown by lines. Dashed line shows equivalent calculation with individual-level testing (i.e., pool of size 1). (B) Scenario where 500 passengers are tested. (C, D) close-up of the boxed region in (A) and (B), focusing on the estimation range covering the prevalence values observed for SARS-CoV-2 in Fig 4. SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

 Discussion

Our analysis shows that it is possible to obtain detailed international epidemiological insights from arrival testing protocols that were designed predominantly as control measure to reduce risk of onwards local transmission. As a proof-of-concept, we reconstructed epidemic dynamics in France and the United States using arrival data from French Polynesia between July 2020 and March 2022.

Raw observed infection prevalence at arrival in French Polynesia was consistent with broad ranges observed in other, smaller-scale studies. Among arrivals into Alaska from June to November 2020, 0.8% were positive [13]. Infection prevalence at arrival was 1.0% in Toronto, Canada during September and October 2020 [15] and 1.5% in Alberta, Canada in November 2020 [14]. However, travellers in these studies did not have their country of origin reported, or information on departure testing protocol, limiting the ability of these datasets to provide comparable information on likely prevalence in countries of departure. Our estimates of peak prevalence in France and the USA were also similar in magnitude to PCR test positivity in the ONS community infection survey in the UK, which peaked at around 2% during the Alpha wave in early 2021 and around 7% during the BA.1 wave in late 2021/early 2022 [7].

A key strength of our analysis is that it uses data from a systematic testing programme with known protocols and countries of departure. Despite the relatively small travel volume into French Polynesia, our real-time estimates of cumulative incidence in France and the USA reflected subsequent changes in national seroprevalence observed in these locations. In contrast, commonly cited international COVID-19 indicators such as cases or hospitalisations are strongly dependent on symptom severity and domestic access to testing, as well as being delayed outcomes relative to infection incidence. Although symptomatic individuals may have avoided travel, test positivity also tends to peak around the time of symptom onset [23], and hence, these individuals would have been detected at departure had they travelled, a process that is already accounted for in the analysis presented here.

In French Polynesia, further data linkage was limited to protect traveller privacy, meaning it was not possible to explore how factors such as age or occupation influenced prevalence. Although our estimates reflected national-level patterns of seroprevalence in the USA and France, the age distribution of travellers may be different to the age distribution of the population at departure, particularly if there are additional constraints on unvaccinated individuals such as children travelling, as occurred from mid-2021 until early 2022. Moreover, travellers may be from wealthier or less-risk averse groups, who may have a different exposure risk to the wider population. Demographic insights could be expanded in future epidemics by routinely reporting data on epidemiologically relevant values such as age and occupation, which would allow adjustment for potential biases when estimating prevalence in countries of departure. Linkage with test results at departure would also enable validation of assumptions about PCR sensitivity at each stage of travel.

There are some additional limitations to our proof-of-concept analysis. The testing protocols in French Polynesia enabled estimation of departure prevalence using a binomial distribution, with little bias from epidemic phase (Fig 2E–2G). In the absence of departure testing, such methods would no longer be reliable, and arrival testing would need to be analysed in the context of changing epidemic dynamics, requiring more complex inference methods that also model the underlying epidemic process [21]. We also assume that PCR positivity over time for the wild-type variant is representative of subsequent variants, based on similarities observed in cohort studies [23,24]. If positivity were to vary substantially, then this would need to be accounted for with different adjustments based on dominant variants in countries of departure. If viral shedding profiles were very different by variant, similar adjustments would also need to be made when inferring infection dynamics from other data sources, such as community infection surveys or wastewater data.

During the initial phase of COVID-19 pandemic, testing among departing and arriving travellers provided valuable indications of the true extent of infection [3], which were in turn used to estimate crucial metrics such as infection fatality risk [25]. Understanding levels of community infection has been challenging both when infections are rising, with the epidemic outstripping symptomatic surveillance capacity, as well as in the later stages of the COVID-19 pandemic, with countries rolling back test availability for symptomatic cases. In the UK, studies such as REACT-1 and ONS have provided continuity in local understanding of community infections, but such data streams have been extremely rare globally, given their expense and logistical complexity. However, our analysis shows that routine traveller testing can enable similar insights at an international scale, using protocols that were already being implemented in many countries, offering a substantial improvement on common situational awareness based on reported cases, hospitalisations, or deaths [4]. Such estimates could also be triangulated against other potential leading indicators, including viral concentrations in wastewater, although interpretation of wastewater data would rely on additional analysis to convert into a comparable measure of the proportion of the population who are currently infected. In turn, these measurements of infection dynamics could be used in estimation of situation awareness metrics such as the reproduction number, as well as estimation of population-level epidemic dynamics for scenario models, where knowledge of the extent of infection can substantially improve statistical inference [26].

Our study provides a proof-of-concept for ongoing COVID-19 management and future pandemic planning, showing that systematic collection of testing data with minimal linkage can enable real-time estimation of underlying epidemic dynamics in multiple countries. Moreover, deployment of such methods in multiple locations—such as key global travel hubs—would allow for synthesis of prevalence estimation across datasets, narrowing uncertainty, and greatly expanding the network of countries covered.

 Supporting information Supplementary methods.

(DOCX)

 Sensitivity of estimation to difference sample sizes and testing assumptions.

(A) Reconstruction of simulated epidemics from arrival testing data, assuming 100 arrivals tested per week. Solid points, “true” prevalence at departure with lines showing 95% binomial confidence interval; squares, measured prevalence at arrival in simulated scenario with a test 2 days before departure with 1 day in transit and another test 4 days post-arrival (i.e., 2d + 4d), with circles showing reconstructed departure prevalence from these data. (B) Same as (A), but with 5,000 arrivals tested per week. (C) Same as (A), but with 20,000 arrivals tested per week. (D) Reconstruction of simulated epidemics from arrival testing data when test timing is mis-specified (shown in green). In the simulation, there is a test 2 days before departure with 1 day in transit and another test on day of arrival. However, inference is performed with a test 2 days before departure with 1 day in transit and another test 4 days post-arrival. (E) Same as (D), but with 5,000 tested per week. (F) Same as (D), but with 20,000 tested per week. (G) Reconstruction of simulated epidemics from arrival testing data when test sensitivity is mis-specified (shown in purple). Simulated tests have peak sensitivity equal to 80% of the peak in our baseline analysis, with inference performed under assumption sensitivity is unchanged. Otherwise, the scenario is same as in (A). (H) Same as (G), but with 5,000 tested per week. (I) Same as (G), but with 20,000 tested per week.

(TIFF)

 Reconstruction of infection dynamics in France and USA from arrival testing data in French Polynesia (FP), assuming antigen tests at departure after May 2021.

Panels otherwise as described in main text (Fig 4).

(TIFF)

 Reconstruction of cumulative incidence under different estimated methods.

(A) Simulated daily incidence. (B) Simulated daily prevalence based on the convolution of incidence in (A) and median probability of PCR positivity in Fig 1B. (C) Reconstruction of incidence from prevalence in (B) using our baseline scaling approximation (red line) as well as deconvolution using the Moore–Penrose generalized inverse (blue line, showing numerical instability at end of time series). (D) Comparison of simulated cumulative incidence (black dots), with estimated cumulative incidence using our baseline scaling approximation (red line) as well as deconvolution using the Moore–Penrose generalized inverse (blue line).

(TIFF)

 Abbreviations COVID-19

Coronavirus Disease 2019

 CrI

credible interval

 ONS

Office for National Statistics

 PCR

polymerase chain reaction

 REACT-1

Real-Time Assessment of Community Transmission

 SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2

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ACS ES&T Water. 2022;2(11):1899–1909. doi: 10.1021/acsestwater.1c00434 36380771 Santé Publique France. COVID-19: études pour suivre la part de la population infectée par le SARS-CoV-2 en France (2021). Available from: https://www.santepubliquefrance.fr/etudes-et-enquetes/covid-19-etudes-pour-suivre- la-part-de-la-population-infectee-par-le-sars-cov-2-en-france. Accessed: 18 August 2022. DREES. 4% de la population a développé des anticorps contre le SARS-CoV-2 entre mai et novembre 2020. Études et Résultats. 2021. Gallian P, Hozé N, Brisbarre N, Saba Villarroel PM, Nurtop E, Isnard C, et al. SARS-CoV-2 IgG seroprevalence surveys in blood donors before the vaccination campaign, France 2020–2021. MedRxiv. 2022. 10.1371/journal.pmed.1004283.r001 Decision Letter 0 Dodd Philippa C. Senior Editor 2023 Philippa C. Dodd This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

9 Jan 2023

Dear Dr Kucharski,

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 10.1371/journal.pmed.1004283.r002 Decision Letter 1 Dodd Philippa C. Senior Editor 2023 Philippa C. Dodd This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

26 Apr 2023

Dear Dr. Kucharski,

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Thanks for your submission and congratulations on proposing a potentially unique case for traveller screening data. Please find below some requests:

(1) Most countries are using environmental surveillance (i.e. wastewater surveillance) as a leading indicator. Would suggest including that triangulation and comparison as part of this submission, if feasible.

(2) Please elaborate on utility of these data for other countries and the larger public health community (e.g. would you propose the UK used these data as a proxy for other countries' transmission levels?).

(3) Please elaborate on potential selection biases involved with using traveller data. A couple examples include that these travellers may only represent the wealthy from US/France rather than travellers across socioeconomic strata and also misses symptomatic individuals.

(4) For the French and US analyses, could you contextualize with serosurveillance and case surveillance data from those countries rather than the UK (the results cites UK infection surveys after the analyses which was confusing)?

(5) There is a lot going on in the figures. I really wanted to hone in on the triangulation of travellers data to nationally representative seroprevalence data. This appears to be in C and D? Are the readers supposed to take away that the trend is the same using both data sources? Furthermore, for E and F are these simply data from other studies? It is not immediately clear how these travellers data are included in those panels

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Comments from the reviewers:

Reviewer #1: Kucharski et al. analyse COVID testing data on persons arriving in French Polynesia, and make useful inferences on infection prevalence in other parts of the world.

Overall comments

This is a very nice and careful analysis of a complex dataset. There are some limitations, and one general question I would have is about the public health value of this data to the people paying for this data (possibly the taxpayers in French Polynesia?), since it relates to infection prevalence in other locations. Nevertheless, countries should be willing to donate resources for the greater international good. In addition, I don't think it is too plausible that COVID policies in France or the United States would be based on surveillance data on travelers into French Polynesia. France and the United States should be capable of collecting data on infection prevalence in their own locations. Notwithstanding these issues, there is clearly incredible scientific value of the data, which were generated anyway because of on-arrival testing of all arrivals. I agree with authors that a global network of sentinel locations collecting this type of data would be a fantastic resource to allow inferences on infection prevalence globally, and WHO should certainly consider this in the next pandemic. However for surveillance purposes, the ideal data would be on-arrival testing and reporting of pre-departure tests, because the shape of the "hole" in Figure 1D is absolutely critical to all of authors' inferences.

Perhaps worthy of mention is that the travel measures in French Polynesia between February and August 2021 was part of a successful effort to keep COVID out of the community (correct, from Figure 3C?) by combining it with on-arrival quarantine as well as other control measures? Perhaps the paper could be expanded with a brief mention of this, because obviously on-arrival COVID testing will only affect COVID control if combined with (1) minimization in daily arrivals and (2) strict management of those that do arrive and (3) domestic outbreak control measures. Without these other three components it is unlikely that arrival testing will have any impact.

One other final comment/concern is the complexity of methodological description used for presentation to a general audience. Authors might consider illustrating the methods with a flow diagram or schematic to show the concepts involved, with formulas left to the appendix.

Major comments

1. Have authors assumed that PCR sensitivity in French Polynesia is equal to PCR sensitivity in the pre-departure tests (mainly in France and the US)? I would question that assumption. PCR sensitivity can vary substantially between laboratories. Figure 1D has a chunk out of it assuming that pre-departure tests have a particular sensitivity, but authors have basically no information on that. Authors also assume constant PCR sensitivity over time for different variants which is somewhat unlikely.

2. In third paragraph of results, at the end, there is an argument about the proportion of infections that would be picked up by delayed testing but the point of this analysis is unclear. Other studies have already shown that unless imported infections are reduced to an absolute trickle, COVID will spread in the community (unless the community is already in lockdown). Delayed on-arrival COVID testing may or may not have an advantage over immediate on-arrival testing but there is a far bigger story to look at there, than just the proportion of infections picked up. This paper seems to be about the inferences that can be drawn on prevalence in France and the US from the arrival testing, not about the value of the arrival testing for the control of COVID in French Polynesia (see comments above).

3. End of first paragraph of results "Our approach could therefore generate reliable estimates for simulated departure prevalence dynamics…" that's good news that your model can recover input parameters in a simulation, but reliability mainly depends on sample size, accuracy would be a more important metric to mention here, particularly in simulations where the pre-departure PCR sensitivity is misspecified in your model. At a guess, the inference on trends in prevalence would be more accurate than the inference on exact prevalence values at a given time, provided that pre-departure PCR sensitivity remains fairly constant over time even if misspecified in the model?

4. For Figure 4, how did you know where travelers came from? Was this just based on where the inbound flight had taken off from, or was it based on some type of health declaration? Some passengers may have connected onto the inbound flights and actually come from other locations? Ref 23 seems to be referring to tourists but many arriving persons would be returning residents? And ref 23 may not survive in the future, it might be better to extract the raw data you used from that website and include it as an appendix file.

5. The end of the fourth paragraph provides UK data for context but that would seem to be better suited to the Discussion than the Results section. I don't think you have any directly comparable data on prevalence in France or the US for the studied periods, which means there is not really a way to validate your results. That does reveal the potential importance of your inferences - if valid - to reveal something that otherwise is not known. But it would be preferable if you had at least some data for France or the United States to triangulate. Comparison of your PCR results with serology is not particularly convincing in this case, unless you can show perhaps how ONS PCR data closely match with ONS seropositivity as well?

Reviewer #2: In this paper, the authors describe results from a SARS-CoV-2 traveler testing program run in French Polynesia between July 2020 and March 2022. As the authors highlight, the use of traveler testing data can potentially provide a less biased estimate of local infection prevalence, serve as a leading indicator for changes in prevalence, and also contain information on the prevalence of countries where travelers originate from. The authors demonstrate an approach for analyzing arrival/departure testing data, accounting for potential biases associated with epidemic stage, e.g., rising, falling steady, and when an individual was tested wrt to travel, e.g., how far in advance of travel or how long after arriving. Additionally, they provide estimates of SARS-CoV-2 prevalence in French Polynesia, France, and the United States. Given the recent attention associated with airport testing, the work here is quite timely. However, I do have a few questions/comments, which I hope the authors find constructive.

1. Do we know how infection rates among travelers is related to infection rates in the entire population? Given that there are socio-economic biases between travelers and non-travelers and socio-economic biases in infection rates, it seems as though some correction is needed because travelers are not an iid sample from the entire population.

2. Additionally, do we know if travelers are more generally at risk of getting infected? You might imagine that the distribution of social contacts for someone who is a traveler is higher than for someone that isn't, so, all else equal, their risk of getting infected would be higher.

3. Could either points 1 or 2 above be related to why you find that traveler positivity is a leading indicator of country-wide prevalence? I could also imagine that simply having a less biased estimate would provide a leading indicator as well.

4. Related to the above points, how do your estimates of prevalence compare to hospitalizations, test positivity, and wastewater positivity (to the extent it's available) in the various countries? Given that we expect the relationship between true prevalence and test positivity to shift during the pandemic, I'd expect to see a similar shift in the relationship between test positivity and your estimate of prevalence from travel testing.

5. Many individuals also believe that wastewater surveillance can provide an unbiased, real-time estimate of prevalence. I would encourage the authors to add a brief discussion of how wastewater surveillance and traveler testing might complement each other. For example, wastewater surveillance also tends to be a leading indicator, but that's almost certainly due to biases in clinical testing wrt to timing of infection. As a result, I could imagine that traveler testing might prove to be a true leading indicator.

6. In the "Prevalence Model" section, the authors provide an estimate of the mean duration of positivity (8.6 days). Was this number obtain empirically? If so, it's not clear how it estimated. If it's not an empirical estimate, where do you obtain this value?

7. Related to the above, is there data on whether infection duration varied across variants? The reported serial interval for Omicron is between 2 and 4 days (although Abbott et al. report a generation time of 1.5 - 3.2 days, which would meant the serial interval would likely be narrower).

Abbott, S., Sherratt, K., Gerstung, M., & Funk, S. (2022). Estimation of the test to test distribution as a proxy for generation interval distribution for the Omicron variant in England. medRxiv.

Kim, D., Ali, S. T., Kim, S., Jo, J., Lim, J. S., Lee, S., & Ryu, S. (2022). Estimation of serial interval and reproduction number to quantify the transmissibility of SARS-CoV-2 omicron variant in South Korea. Viruses, 14(3), 533.

Ito, K., Piantham, C., & Nishiura, H. (2022). Estimating relative generation times and relative reproduction numbers of Omicron BA. 1 and BA. 2 with respect to Delta in Denmark. medRxiv.

Reviewer #3:

This paper is concerned with a potentially useful method for surveillance of covid prevalence using traveller screening data. While the idea and development are interesting, there is a large number of methodological issues with the paper. Specific comments are given below.

The authors will need to be scientifically honest about the quantification of uncertainty of the proposed approach. At a somewhat simplistic level they suggest that their data, which contain information from about a thousand cases, can reconstruct and accurately estimate the epidemic dynamics of the USA and France. If this method is to be used in practice one would need a precise estimate of the associated uncertainty at the very least.

This task also involves estimating the uncertainty of functions of probabilities and explicit discussions on using the bootstrap or the delta method should be given.

In addition to the variance of those probabilities, potential bias issues should be discussed in detail, especially since some of the probabilities involved are small in which case the associated MLEs/sample proportions can be highly unstable. For such cases, methods like the Firth correction or Bayesian approaches give more accurate estimates (in terms of mean square error) and could be considered in this paper.

In addition to the technical details of the bias and variance properties of the estimates used, there is a subtle notion related to the aim of the paper. Are the travellers used for screening a truly representative sample of the population at the country of origin? And if not does this really matter?

It would be desirable to disentangle the two and offer a fair account of what the paper does and does not achieve with the proposed approach.

Please discuss reliable ways to validate these estimates, including against independent data, possibly from seroprevalence studies. The "bootstrap prediction intervals" discussed at the end of "prevalence model" are in-sample estimates and therefore conservative.

Do the authors account for the effects of false positives/negatives and the associated uncertainty?

Please discuss the availability of the various data sources, including the cohort study of self-tested healthcare workers.

I think the authors implicitly assume in their calculations that the probability of infection during travel is negligible, please discuss.

Would working with R_t estimates offer an alternative or complementary approach to the proposed technique? In some ways it would settle some of the issues like automatically adjusting for a growing/declining epidemic in the country of origin.

When discussing the results (like the 60/40 contribution in figure 3) it would be useful to try and disentangle the relative importance of assumptions and data. Some of those results heavily depend upon the assumption made so some clarification would help.

A similar idea, of exploring traveller screening data for surveillance is explored in (Bastani et al. Nature 599, p.108-113, 2021) where additional covariate information is utilised. The authors should discuss their work in the context of the relevant literature.

Page numbering would be useful in the review process, please add them.

Any attachments provided with reviews can be seen via the following link:

[LINK]

 10.1371/journal.pmed.1004283.r003 Author response to Decision Letter 1 Submission Version 2

16 Jun 2023

Submitted filename: Response_to_reviewers_V2_1.docx

 10.1371/journal.pmed.1004283.r004 Decision Letter 2 Dodd Philippa C. Senior Editor 2023 Philippa C. Dodd This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 2

10 Aug 2023

Dear Dr. Kucharski,

Thank you very much for re-submitting your manuscript "Real-time surveillance of international SARS-CoV-2 prevalence using systematic traveller arrival screening: a retrospective study" (PMEDICINE-D-22-04017R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Aug 17 2023 11:59PM.   

Sincerely,

Philippa Dodd, MBBS MRCP PhD

Senior Editor 

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------------------------------------------------------------

Requests from Editors:

GENERAL

Thank you for your detailed and considered responses to previous editor and comments. Please see below for further comments that we require you address prior to publication.

TITLE

Please replace ‘a retrospective…’ with ‘an observational…’

AUTHOR SUMMARY

Lines 63 onwards suggest removing the CIs to improve accessibility to the non-scientific reader.

Line 72 – this statement is rather vague and doesn’t really tell us anything specific. Suggest removing.

METHODS

Line 153 - please define PCR at first use in the main manuscript.

FIGURES

If this is not possible to begin axes of graphs at zero, please show a break in the axis.

Figure 4 caption – please define ‘GAM’ for the reader.

DISCUSSION

Line 354 – suggest ‘using arrival data from French Polynesia between July 2020 and March 2022’ instead.

REFERENCES

Please ensure all web references include an accessed date.

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Please cite your Supporting Information as outlined here: https://journals.plos.org/plosmedicine/s/supporting-information

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To help us extend the reach of your research, please detail any Twitter handles you wish to be included when we tweet this paper (including your own, your coauthors’, your institution, funder, or lab) in the manuscript submission form when you re-submit your manuscript.

COMMENTS FROM THE ACADEMIC EDITOR

The authors have done a comprehensive job in responding to all editorial and reviewer comments. The figure and legends of figures can use clarification

Comments from Reviewers:

Reviewer #1: I am satisfied with previous responses and corresponding revisions, and I have no further comments

Reviewer #3: The authors have revised their paper and this is now a substantially improved manuscript.

The response to my comment about validating the paper's estimates was cut but the overall response was satisfactory so I have no further comments.

Any attachments provided with reviews can be seen via the following link:

[LINK]

 10.1371/journal.pmed.1004283.r005 Author response to Decision Letter 2 Submission Version 3

16 Aug 2023

Submitted filename: Response_to_reviewers_V3.docx

 10.1371/journal.pmed.1004283.r006 Decision Letter 3 Dodd Philippa C. Senior Editor 2023 Philippa C. Dodd This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 3

22 Aug 2023

Dear Dr Kucharski, 

On behalf of my colleagues and the Academic Editor, Dr. Amitabh Suthar, I am pleased to inform you that we have agreed to publish your manuscript "Real-time surveillance of international SARS-CoV-2 prevalence using systematic traveller arrival screening: an observational study" (PMEDICINE-D-22-04017R3) in PLOS Medicine.

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Best wishes, 

Philippa Dodd, MBBS MRCP PhD 

PLOS Medicine