Real-time surveillance of international SARS-CoV-2 prevalence using systematic traveller arrival screening: An observational study

Abstract

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.

Fig 1. 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.

Fig 2. 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.

Fig 3. 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.

Fig 4. 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.

Fig 5. 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.