ABSTRACT
Quarantining close contacts of individuals infected with SARS-CoV-2 for 10 to 14 days is a key strategy in reducing transmission. However, quarantine requirements are often unpopular, with low adherence, especially when a large fraction of the population has been vaccinated. Daily contact testing (DCT), in which contacts are required to isolate only if they test positive, is an alternative to quarantine for mitigating the risk of transmission from traced contacts. In this study, we developed an integrated model of COVID-19 transmission dynamics and compared the strategies of quarantine and DCT with regard to reduction in transmission and social/economic costs (days of quarantine/self-isolation). Specifically, we compared 10-day quarantine to 7 days of self-testing using rapid lateral flow antigen tests, starting 3 days after exposure to a case. We modelled both incomplete adherence to quarantine and incomplete adherence to DCT. We found that DCT reduces transmission from contacts with similar effectiveness, at much lower social/economic costs, especially for highly vaccinated populations. The findings were robust across a spectrum of scenarios with varying assumptions on the speed of contact tracing, sensitivity of lateral flow antigen tests, adherence to quarantine and uptake of testing. Daily tests would also allow rapid initiation of a new round of tracing from infected contacts.
ABSTRACT
ObjectiveTo measure meaningful, local exposure notification usage without in-app analytics. MethodsWe surveyed app usage via case investigation interviews at the University of Arizona, with a focus on the period from September 9 to November 28, 2020, after automating the issuance of secure codes to verify positive test results. As independent validation, we compared the number of verification codes issued to the number of local cases. ResultsForty six percent (286/628) of infected persons interviewed by university case investigators reported having the app, and 55% (157/286) of these app users shared their positive SARS-CoV-2 test result in the app prior to the case investigation interview, comprising 25% (157/628) of those interviewed. This is corroborated by a 33% (565/1,713) ratio of code issuance (inflated by some unclaimed codes) to cases. Combining the 25% probability that those who test positive rapidly share their test result with a 46% probability that a person they infected can receive exposure notifications, an estimated 11.4% of transmission pairs exhibit meaningful app usage. High usage was achieved without the use of "push" notifications, in the context of a marketing campaign that leveraged social influencers. ConclusionsUsage can be assessed, without in-app analytics, within a defined local community such as a college campus rather than an entire jurisdiction. With marketing, high uptake in dense social networks like universities makes exposure notification an impactful complement to traditional contact tracing. Integrating verification code delivery into patient results portals was successful in making the exposure notification process rapid. 3 question summary box1) What is the current understanding of this subject?The extent to which exposure notification technology reduces SARS-CoV-2 transmission depends on usage among infected persons. 2) What does this report add to the literature?A novel metric estimates meaningful usage, and demonstrates potential transmission reduction on a college campus. Clear benefit was seen from simplifying verification of positive test results with automation. 3) What are the implications for public health practice?Defined communities can benefit from local deployment and marketing even in the absence of statewide deployment. Lifting current restrictions on deployment would allow more entities such as campuses to copy the model shown here to be successful.
ABSTRACT
1.Some infectious diseases, such as COVID-19, are so harmful that they justify broad scale social distancing. Targeted quarantine can reduce the amount of indiscriminate social distancing needed to control transmission. Finding the optimal balance between targeted vs. broad scale policies can be operationalized by minimizing the total amount of social isolation needed to achieve a target reproductive number. Optimality is achieved by quarantining on the basis of a risk threshold that depends strongly on current disease prevalence, suggesting that very different disease control policies should be used at different times or places. Aggressive quarantine is warranted given low disease prevalence, while populations with a higher base rate of infection should rely more on social distancing by all. The total value of a quarantine policy rises as case counts fall, is relatively insensitive to vaccination unless the vaccinated are exempt from distancing policies, and is substantially increased by the availability of modestly more information about individual risk of infectiousness.
ABSTRACT
The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non-pharmaceutical interventions on the spread of COVID-19 epidemics. We examined the distribution of transmission events with respect to exposure and onset of symptoms. We show that for symptomatic individuals, the timing of transmission of SARS-CoV-2 is more strongly linked to the onset of clinical symptoms of COVID-19 than to the time since infection. We found that it was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. However, we caution against overinterpretation of the right tail of the distribution, due to its dependence on behavioural factors and interventions. We also found that the pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. This strongly suggests that information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41%; 95%CI 31-50%), which limits the efficacy of symptom-based interventions, and the large fraction of transmissions (35%; 95%CI 26-45%) that occur on the same day or the day after onset of symptoms underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if they are mild. Rapid or at-home testing and contextual risk information would greatly facilitate efficient early isolation.
ABSTRACT
Most Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, [≥]15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. 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 the Google/Apple v1.1, and [~]11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set 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.