Quantifying SARS-CoV-2 infection risk within the Google/Apple exposure notification framework to inform quarantine recommendations

Abstract

Background: Bluetooth-based exposure notification apps can supplement manual contact tracing to reduce SARS-CoV-2 transmission. Their speed, scalability, and privacy preservation are generally acknowledged, but less exploited are smartphones' accurate measurement of duration, and ability to automatically calculate risk from multiple inputs. Methods: We model uncertainty in the shape of an exhaled virus-containing plume, inhalation parameters, and distance as a function of Bluetooth attenuation. We assume relative rates of viral shedding depend on the timing of exposure relative to symptom onset. We calibrate an exponential dose-response curve on the basis of the infection probabilities of household contacts. The conditional probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been free of symptoms, decreases during quarantine, with shape determined by the distribution of incubation periods, proportion of asymptomatic cases, and distribution of asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. Findings: As an example of our calculations, fifteen minutes of close contact with a high-shedding individual, given a 15% asymptomatic infection rate and no testing, would require 5- and 14-day quarantine for their risk of current or future infectiousness to fall below 0.84% and 0.14% risk, respectively. Interpretation: The Covid-Watch app is currently programmed either to use a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration. Either threshold can be set by public health authorities.

Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

“…One should prioritize these individual cases for tracing since their contacts are likely to be earlier in their infections and quarantining them will cut off most or all of their infectious period. If one knows the date of contact between the case and the contact, one could also prioritize tracing more recent contacts and those that had contact with the case during the case's days of peak infectiousness just before and after symptom onset (26,27). Second, if one is attempting to limit transmission in multiple regions (e.g.…”

The economic value of quarantine is higher at lower case prevalence, with quarantine justified at lower risk of infection

“…We note that the value of an exposure notification app would be dramatically higher if, instead of a blanket 14 day quarantine duration, a shorter quarantine, targeted to the days of greatest risk, were used [7]. Further gains would come from using negative test results to shorten quarantine.…”

“…For this work, it is assumed that people under consideration for quarantine have an estimated risk of infectiousness. This could be estimated using proximity and duration of contact with a known case as described in [7]. Or it could be estimated for members of a sub-population like a workplace by rapid testing of a random sample of that sub-population and projecting the proportion positive onto the remainder.…”

“…Several studies have found the secondary attack rate to be on the order of 1/10 - 1/100 [15, 16, 17], so this risk threshold aligns well with standard guidelines for the minimum contact considered close for tracing purposes. We note that 14 day quarantine is more than is needed to put the conditional probability of infectiousness, given lack of symptoms, of a non-household close contact below the base rate [7].…”

Quantifying meaningful usage of a SARS-CoV-2 exposure notification app on the campus of the University of Arizona

“…Smartphone applications (apps) for exposure notification have the potential, given sufficient uptake, to significantly reduce the spread of SARS-CoV-2, by making contact tracing faster, more scalable, and potentially more acceptable due to greater privacy and ease of use (1). The University of Arizona community piloted the Covid Watch app, which through the Google/Apple Exposure Notification API (GAEN), uses Bluetooth to measure date, distance, and duration of contact, and assess infection risk (2). App users who tested positive for SARS-CoV-2 were able to anonymously trigger notifications, including testing and quarantine recommendations, for other Covid Watch users with whom they had been in contact.…”