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One-year surveillance of SARS-CoV-2 transmission of the ELISA cohort: A model for population-based monitoring of infection risk.

Christine KleinMax BorscheAlexander BalckBandik FöhJohann RahmöllerElke PetersJan KnickmannMiranda LaneEva-Juliane VollstedtSusanne A ElsnerNadja KädingSusanne HauswaldtTanja LangeJennifer E HundtSelina LehrianJulia GieseAlexander MischnikStefan NiemannFlorian P MaurerSusanne HomolkaLaura PaulowskiJan KramerChristoph TwestenChristian SinaGabriele Gillessen-KaesbachHauke BuschMarc EhlersStefan TaubeJan RuppAlexander Katalinic
Published in: Science advances (2022)
With newly rising coronavirus disease 2019 (COVID-19) cases, important data gaps remain on (i) long-term dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates in fixed cohorts (ii) identification of risk factors, and (iii) establishment of effective surveillance strategies. By polymerase chain reaction and antibody testing of 1% of the local population and >90,000 app-based datasets, the present study surveilled a catchment area of 300,000 inhabitants from March 2020 to February 2021. Cohort (56% female; mean age, 45.6 years) retention was 75 to 98%. Increased risk for seropositivity was detected in several high-exposure groups, especially nurses. Unreported infections dropped from 92 to 29% during the study. "Contact to COVID-19-affected" was the strongest risk factor, whereas public transportation, having children in school, or tourism did not affect infection rates. With the first SARS-CoV-2 cohort study, we provide a transferable model for effective surveillance, enabling monitoring of reinfection rates and increased preparedness for future pandemics.
Keyphrases
  • respiratory syndrome coronavirus
  • sars cov
  • coronavirus disease
  • risk factors
  • public health
  • mental health
  • healthcare
  • young adults
  • machine learning
  • big data
  • current status
  • electronic health record
  • data analysis