PreprintCited by 3SARS-CoV-2 detection and testingCOVID-19 epidemiological studiesSARS-CoV-2 and COVID-19 Research

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Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial (Preprint)

Open access

Preprint — published as 10.2196/44204

Abstract

BACKGROUND: The COVID-19 pandemic is characterized by rapid increases in infection burden owing to the emergence of new variants with higher transmissibility and immune escape. To date, monitoring the COVID-19 pandemic has mainly relied on passive surveillance, yielding biased epidemiological measures owing to the disproportionate number of undetected asymptomatic cases. Active surveillance could provide accurate estimates of the true prevalence to forecast the evolution of the pandemic, enabling evidence-based decision-making. OBJECTIVE: This study compared 4 different approaches of active SARS-CoV-2 surveillance focusing on feasibility and epidemiological outcomes. METHODS: A 2-factor factorial randomized controlled trial was conducted in 2020 in a German district with 700,000 inhabitants. The epidemiological outcome comprised SARS-CoV-2 prevalence and its precision. The 4 study arms combined 2 factors: individuals versus households and direct testing versus testing conditioned on symptom prescreening. Individuals aged ≥7 years were eligible. Altogether, 27,908 addresses from 51 municipalities were randomly allocated to the arms and 15 consecutive recruitment weekdays. Data collection and logistics were highly digitized, and a website in 5 languages enabled low-barrier registration and tracking of results. Gargle sample collection kits were sent by post. Participants collected a gargle sample at home and mailed it to the laboratory. Samples were analyzed with reverse transcription loop-mediated isothermal amplification (RT-LAMP); positive and weak results were confirmed with real-time reverse transcription–polymerase chain reaction (RT-PCR). RESULTS: Recruitment was conducted between November 18 and December 11, 2020. The response rates in the 4 arms varied between 34.31% (2340/6821) and 41.17% (2043/4962). The prescreening classified 16.61% (1207/7266) of the patients as COVID-19 symptomatic. Altogether, 4232 persons without prescreening and 7623 participating in the prescreening provided 5351 gargle samples, of which 5319 (99.4%) could be analyzed. This yielded 17 confirmed SARS-CoV-2 infections and a combined prevalence of 0.36% (95% CI 0.14%-0.59%) in the arms without prescreening and 0.05% (95% CI 0.00%-0.108%) in the arms with prescreening (initial contacts only). Specifically, we found a prevalence of 0.31% (95% CI 0.06%-0.58%) for individuals and 0.35% (95% CI 0.09%-0.61%) for households, and lower estimates with prescreening (0.07%, 95% CI 0.0%-0.15% for individuals and 0.02%, 95% CI 0.0%-0.06% for households). Asymptomatic infections occurred in 27% (3/11) of the positive cases with symptom data. The 2 arms without prescreening performed the best regarding effectiveness and accuracy. CONCLUSIONS: This study showed that postal mailing of gargle sample kits and returning home-based self-collected liquid gargle samples followed by high-sensitivity RT-LAMP analysis is a feasible way to conduct active SARS-CoV-2 population surveillance without burdening routine diagnostic testing. Efforts to improve participation rates and integration into the public health system may increase the potential to monitor the course of the pandemic. CLINICALTRIAL: Deutsches Register Klinischer Studien (DRKS) DRKS00023271; https://tinyurl.com/3xenz68a INTERNATIONAL REGISTERED REPORT: RR2-10.1186/s13063-021-05619-5

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Research works printed but not linked: 23

  1. 10.1016/j.vaccine.2015.06.090 · DOI
  2. 10.1126/science.abi5273 · DOI
  3. 10.1016/s1473-3099(20)30482-5 · DOI
  4. 10.1038/s41564-021-01029-0 · DOI
  5. 10.1038/s41591-021-01472-w · DOI
  6. 10.1093/ajcp/aqaa099 · DOI
  7. 10.1101/2021.06.17.21259103 · DOI
  8. 10.1016/s2214-109x(20)30213-8 · DOI
  9. 10.1038/s41598-021-84145-5 · DOI
  10. 10.2196/21364 · DOI
  11. 10.1101/2020.10.26.20219428 · DOI
  12. 10.1038/s41467-021-22351-5 · DOI
  13. 10.1186/s13063-021-05619-5 · DOI
  14. 10.2807/1560-7917.es.2020.25.3.2000045 · DOI
  15. 10.1101/2020.05.05.20092288 · DOI
  16. 10.1136/bmj.i2016 · DOI · Paper page
  17. 10.1371/journal.pone.0255513 · DOI
  18. 10.1080/01621459.1952.10483446 · DOI · Paper page
  19. 10.1101/2020.12.15.20248244 · DOI
  20. 10.3310/phr07080 · DOI
  21. 10.1016/j.pec.2018.03.021 · DOI
  22. 10.1101/2021.04.13.21255345 · DOI
  23. 10.2471/blt.20.257188 · DOI

Metadata

Authors
Deckert, A., Anders, S., Morales, I., De Allegri, M., Nguyen, H. T., Souares, A., McMahon, S., Meurer, M., Burk, R., Lou, D., Brugnara, L., Sand, M., Koeppel, L., Maier-Hein, L., Ross, T., Adler, T. J., Brenner, S., Dyer, C., Herbst, K., Ovchinnikova, S., Marx, M., Schnitzler, P., Knop, M., Bärnighausen, T., & Denkinger, C. M.
Affiliations
  • Heidelberg Institute of Global Health (DE)
  • University Hospital Heidelberg (DE)
  • GESIS - Leibniz Institute for the Social Sciences (DE)
  • German Cancer Research Center (DE)
Corresponding author
Deckert, Andreas
Venue
JMIR Public Health and Surveillance
Published
2022-11-14
Volume
9
Pages
e44204-e44204
ISSN
2369-2960 (ISSN-L)
OpenAlex
W4378473038
Cited by
3
References
1
Citations by year
2025: 1 · 2024: 2
FWCI
0.62
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Public Health and Surveillance (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
Keywords
active surveillance, SARS-CoV-2, COVID-19 pandemic, prevalence estimation, RT-LAMP, RT-PCR validation, asymptomatic infection, Germany, SARS-CoV-2 variants, immune evasion, response rate
MeSH terms
COVID-19, SARS-CoV-2, Humans, Laboratories, Specimen Handling, Pandemics

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