Threshold analyses on combinations of testing, population size, and vaccine coverage for COVID-19 control in a university setting

Abstract

We simulated epidemic projections of a potential COVID-19 outbreak in a university population of 38,000 persons, under varying combinations of mass test rate (0% to 10%), contact trace and test rate (0% to 50%), transmission rate (probability of transmission per contact per day), and contact rate (number of contacts per person per day). We simulated four levels of transmission rate, 14% (average baseline), 8% (average for face mask use), 5.4% (average for 3ft distancing), and 2.5% (average for 6ft distancing and face mask use), interpolating results to the full range to understand the impact of uncertainty in effectiveness, feasibility, and adherence of face mask use and physical distancing. We evaluated contact rates between 1 and 25, to identify the threshold that, if exceeded, could lead to several deaths. When transmission rate was 8%, for trace and test at 50%, the contact rate threshold was 8. However, any time delays in trace, test, and isolation quickly raised the number of deaths. Keeping contact rate to 3 or below was more robust to testing delays, keeping deaths below 1 up to a delay of 5 days from the time of infection to diagnosis and isolation. For a contact rate of 3, the number of trace and tests peaked to about 70 per day and relaxed to 25 with the addition of 10% mass test. When transmission rate was 5.4%, for trace and test at 50%, the contact rate threshold was 10. However, keeping contact rate to 4 or below was more robust to delays in testing, keeping deaths below 1 up to a delay of 6 days from the time of infection to diagnosis and isolation. For contact rate of 4, the number of trace and tests peaked at 50 per day and relaxed to 10 per day with the addition of 10% mass test. Threshold estimates can help develop on-campus scheduling and indoor-spacing plans in conjunction with plans for asymptomatic testing for COVID-19. Combination thresholds should be selected specific to the setting based on an assessment of the feasibility and resource 48 availability for testing and quarantine.

Impact of routine asymptomatic screening on COVID-19 incidence in a highly vaccinated university population

“…Our results agree with previous studies that quantify the impact of mass testing as a strategy to mitigate COVID-19 during return to campus when NPIs are in place and there is partial vaccination coverage. These studies [1,2,16,18,19] find that mass testing provides a small expected reduction in the number of cases for a population with high vaccination coverage. Masking and physical distancing may be efficient strategies to control the spread of COVID-19 in university settings that already have substantial vaccination coverage [1,2,16].…”

“…Some utilized social mixing data from contact surveys [1,9,15,16], whereas others relied on simulations of daily behavioural contacts among students [17]. Only a few of these studies have considered scenarios that include a highly vaccinated university population [1,2,16,18,19].…”

“…provides a summary of recent studies, which show that masking and social distancing largely suffice as protective policies in a partially vaccinated population[1,18]. Zhao et al conclude that a full university population can safely return to campus if at least 95% are vaccinated[18].…”

“…provides a summary of recent studies, which show that masking and social distancing largely suffice as protective policies in a partially vaccinated population[1,18]. Zhao et al conclude that a full university population can safely return to campus if at least 95% are vaccinated[18]. Yang et al find that a vaccine coverage of > 64% would lead to less than 5% of the university population becoming infected, even with no routine testing[1].…”

Serological testing in addition to PCR screening for the re-opening of American colleges and universities: potential for cost-savings without compromising pandemic mitigation

“…Our study shows that cumulative incidence may be the most important intervenable determinant of cost-savings when including serology testing to PCR testing condition. Several previous studies 1,10,11 assessed PCR-based SARS-CoV-2 screening strategies – with varying test frequency and sensitivity (and offered interactive tools to project cumulative incidence 12,13 ) – for university settings in the United States. They suggested testing frequency was more strongly associated with cumulative infection than test sensitivity.…”