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.

Reconstructing the COVID-19 incidence in India using airport screening data in Japan

“…If exposure occurs shortly before departure or if suspicious symptoms occur prior to the departure time, an individual may cancel their travel plans. Third, travelers were less likely to be experiencing COVID-19 symptoms, so the travelers tested by the border screening are likely over-represented by asymptomatically infected or uninfected individuals, who have a lower positivity than the general public, leading to underestimation of incidence as well [ 43 ]. Again, we may have underestimated the incidence if only healthy individuals were sampled as international travellers.…”

Inference of epidemic dynamics in the COVID-19 era and beyond

“…An additional data stream that can provide insight is well-defined populations that have undergone intensive testing for infection, such as passenger evacuation flights out of Wuhan [12,13]. As the COVID-19 pandemic progressed, these initially opportunistic efforts were supplemented by systematic studies, such as the ONS and REACT-1 community testing studies in the UK [14,15], workplace testing at the Crick [6] and in the NBA [16], PHIRST-C in South Africa [7], and travel testing in French Polynesia [17] and major sporting events [18]. Whereas PHIRST was an unusually detailed study of respiratory infections pre-COVID, such data became much more common during the pandemic, enabling more detailed analysis than has historically been possible.…”

Optimization and performance analytics of global aircraft-based wastewater surveillance networks

“…We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.Recent health crises have highlighted the dual role of airports both in spreading infectious diseases globally and simultaneously acting as convenient frontlines for detecting and monitoring emerging health threats [1][2][3][4][5]. In this context, aircraft-based wastewater surveillance is gaining increasing scientific and operational interest.…”

“…Recent health crises have highlighted the dual role of airports both in spreading infectious diseases globally and simultaneously acting as convenient frontlines for detecting and monitoring emerging health threats [1][2][3][4][5]. In this context, aircraft-based wastewater surveillance is gaining increasing scientific and operational interest.…”