Association of SARS-CoV-2 Positivity in Travelers and Community Prevalence: Serial Cross-Sectional Data From the Traveler-based Genomic Surveillance Program
Objectives: As SARS-CoV-2 testing has declined, estimating disease prevalence has become challenging. The Centers for Disease Control and Prevention’s Traveler-based Genomic Surveillance (TGS) program collects voluntary samples from arriving international travelers to detect SARS-CoV-2 variants. We assessed the correlation between SARS-CoV-2 positivity in travelers and community case data for their corresponding countries of origin. Methods: From December 6, 2021, through January 22, 2023, travelers (aged ≥18 y) arriving at selected US airports provided self-collected nasal swabs, which were pooled by country of origin and tested for SARS-CoV-2 using reverse transcription polymerase chain reaction. We compared weekly SARS-CoV-2 positivity rates from travelers who arrived from 6 countries (Brazil, France, Germany, India, South Africa, United Kingdom) with weekly community case rates for that same country. For the United Kingdom, we also compared traveler data with data from the UK Coronavirus (COVID-19) Infection Survey. We conducted correlation analyses by country and divided data into intervals to control for time-varying confounders. Using Spearman rank correlation, we assessed the correlations between traveler data and community or household data. Results: SARS-CoV-2 positivity in travelers and community case data frequently demonstrated concordance (66.7% of time intervals were significantly correlated, defined as P < .05). After June 12, 2022, when predeparture SARS-CoV-2 testing was no longer required of US-bound travelers, sample positivity among travelers arriving from the United Kingdom was significantly correlated with positivity estimates from the UK COVID-19 Infection Survey ( r s = 0.76; P < .001). Conclusions: Data from travelers may reflect trends similar to country-level SARS-CoV-2 transmission. When community testing is limited, public health authorities could consider using TGS or similar systems to monitor transmission activity.
Authors
- Stephen M. Bart (ORCID: https://orcid.org/0000-0001-5501-6286)
- Allison Taylor Walker (ORCID: https://orcid.org/0000-0002-2398-4239)
- Andrew P. Rothstein (ORCID: https://orcid.org/0000-0002-1329-8702)
- Jessica E. Rothman (ORCID: https://orcid.org/0000-0002-9961-1092)
- Benjamin H. Rome
- Scott W. Olesen (ORCID: https://orcid.org/0000-0001-5400-4945)
- Teresa Smith (ORCID: https://orcid.org/0000-0002-1792-2664)
- Sarah Anne J. Guagliardo (ORCID: https://orcid.org/0000-0002-4217-8195)
- Robert C. Morfino
- Cindy R. Friedman (ORCID: https://orcid.org/0000-0003-1583-4617)
- Lauren Gardner
- Samantha Loh (ORCID: https://orcid.org/0000-0002-5784-9748)
- Siyao Lisa Li (ORCID: https://orcid.org/0009-0001-0450-6282)
- Ezra Ernst
- Nathan D. Grubaugh
Institutions
- Oak Ridge Associated Universities (US)
- Centers for Disease Control and Prevention (US)
- Johns Hopkins University (US)
- Emory University (US)
- Yale University (US)
- Ginkgo Bioworks, Inc. (United States) (US)
- National Center for Emerging and Zoonotic Infectious Diseases (US)
- The Centers (US)
- McGraw-Hill Education (United States) (US)
- Oak Ridge Institute for Science and Education
Publication Details
- Journal
- Public Health Reports
- Published
- 2026-09-17
- DOI
- https://doi.org/10.1177/00333549261477721
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centers for Disease Control and Prevention