Webinar Speakers: Joanna Masel, Professor of Epidemiology, University of AZ
Prof. Joanna Masel from the University of Arizona will be taking us into a deep dive around her risk model for GAEN apps. She’ll cover how she helped LFPH Associate Member Covid Watch implement a risk model that’s designed to capture meaningful exposures while doing the least harm possible. After her presentation we’ll have time for Q&A.
As with our last Implementer’s Forum event, we will be following Chatham House Rule (https://www.chathamhouse.org/chatham-house-rule).
Quantifying risk of SARS-CoV-2 transmission for use in the Covid Watch app
We describe a method to integrate Bluetooth attenuation, exposure duration, and exposure timing to estimate the probability of infection within the GAEN framework. To do so, we modeled uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and we measured uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness, which is strongly predicted by timing relative to symptom onset. We calibrate an exponential dose-response curve on the basis of the infection probabilities of household contacts. We also provide a method to calculate the probability of current or future infectiousness, which is a function of initial infection risk, the number of symptom-free days since exposure, and any negative test results. Public health authorities can use either probability, currently implemented in the Covid Watch app, to apply a threshold for quarantine. We capture a 10-fold range of risk using 6 infectiousness values, ~11-fold range using 3 Bluetooth attenuation bins, ~6-fold range from exposure duration (given the 30 minute duration cap imposed by GAEN v1), and ~11-fold between the beginning and end of 14 day quarantine.