Selected article for: "estimate model and infection case"

Author: Vazquez, A.; Staebler, M.; Khanin, A.; Lichte, D.; Brucherseifer, E.
Title: Estimating the super-spreading rate at workplaces using bluetooth technology
  • Cord-id: xdxllww6
  • Document date: 2021_3_8
  • ID: xdxllww6
    Snippet: Workplaces deploy internal guidelines to remain operational during the ongoing COVID-19 pandemic. It is challenging to assess whether those interventions will prevent super-spreading events, where an infected individual transmits the disease to 10 or more secondary cases. Here we provide a model of infectious disease at the level of a workplace to address that problem. We take as input proximity contact records based on bluetooth technology and the infectious disease parameters from the literatu
    Document: Workplaces deploy internal guidelines to remain operational during the ongoing COVID-19 pandemic. It is challenging to assess whether those interventions will prevent super-spreading events, where an infected individual transmits the disease to 10 or more secondary cases. Here we provide a model of infectious disease at the level of a workplace to address that problem. We take as input proximity contact records based on bluetooth technology and the infectious disease parameters from the literature. Using proximity contact data for a case-study workplace and an infection transmission model, we estimate the SARS-CoV-2 transmission rate as 0.014 per proximity contact, going up to 0.041 for the SARS-CoV-2 B.1.1.7 variant first detected in the UK. Defining super-spreading as events with 10 or more secondary infections, we obtain a super-spreading event rate of 2.3 per 1000 imported SARS-CoV-2 cases, rising up to 13.7 for SARS-CoV-2 B.1.1.7. This methodology provides the means for workplaces to determine their internal super-spreading rate or other infection related risks.

    Search related documents:
    Co phrase search for related documents
    • Try single phrases listed below for: 1