Mitigation of SARS-CoV-2 Transmission at a Large Public University

In the Fall of 2020, many universities saw extensive transmission of SARS-CoV-2 among their populations, threatening the health of students, faculty and staff, the viability of in-person instruction, and the health of surrounding communities.1, 2 Here we report that a multimodal "SHIELD: Target, Test, and Tell" program mitigated the spread of SARS-CoV-2 at a large public university, prevented community transmission, and allowed continuation of in-person classes amidst the pandemic. The program combines epidemiological modelling and surveillance (Target); fast and frequent testing using a novel and FDA Emergency Use Authorized low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD (Test); and digital tools that communicate test results, notify of potential exposures, and promote compliance with public health mandates (Tell). These elements were combined with masks, social distancing, and robust education efforts. In Fall 2020, we performed more than 1,000,000 covidSHIELD tests while keeping classrooms, laboratories, and many other university activities open. Generally, our case positivity rates remained less than 0.5%, we prevented transmission from our students to our faculty and staff, and data indicate that we had no spread in our classrooms or research laboratories. During this fall semester, we had zero COVID-19-related hospitalizations or deaths amongst our university community. We also prevented transmission from our university community to the surrounding Champaign County community. Our experience demonstrates that multimodal transmission mitigation programs can enable university communities to achieve such outcomes until widespread vaccination against COVID-19 is achieved, and provides a roadmap for how future pandemics can be addressed.

Entry screening and multi-layer mitigation of COVID-19 cases for a safe university reopening

We have performed detailed modeling of the COVID-19 epidemic within the State of Illinois at the population level, and within the University of Illinois at Urbana-Champaign at a more detailed level of description that follows individual students as they go about their educational and social activities. We ask the following questions: O_LIHow many COVID-19 cases are expected to be detected by entry screening? C_LIO_LIWill this initial "bump" in cases be containable using the mitigation steps being undertaken at UIUC? C_LI Our answers are: O_LIAssuming that there are approximately 45,000 students returning to campus in the week beginning August 15, 2020, our most conservative estimate predicts that a median of 270 {+/-} 90 (minimum-maximum range) COVID-19 positive cases will be detected by entry screening. The earliest estimate for entry screening that we report was made on July 24th and predicted 198 {+/-} 90 (68% CI) positive cases. C_LIO_LIIf the number of returning students is less, then our estimate just needs to be scaled proportionately. C_LIO_LIThis initial bump will be contained by entry screening initiated isolation and contact tracing, and once the semester is underway, by universal masking, a hybrid teaching model, twice-weekly testing, isolation, contact tracing, quarantining and the use of the Safer Illinois exposure notification app. C_LI

Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19

Epidemics generally spread through a succession of waves that reflect factors on multiple time-scales. On short time-scales, superspreading events lead to burstiness and overdispersion, while long-term persistent heterogeneity in susceptibility is expected to lead to a reduction in the infection peak and the herd immunity threshold (HIT). Here, we develop a general approach to encompass both time-scales, including time variations in individual social activity, and demonstrate how to incorporate them phenomenologically into a wide class of epidemiological models through parameterization. We derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. We show that a state of transient collective immunity (TCI) emerges well below the HIT during early, high-paced stages of the epidemic. However, this is a fragile state that wanes over time due to changing levels of social activity, and so the infection peak is not an indication of herd immunity: subsequent waves can and will emerge due to behavioral changes in the population, driven (e.g.) by seasonal factors. Transient and long-term levels of heterogeneity are estimated by using empirical data from the COVID-19 epidemic as well as from real-life face-to-face contact networks. These results suggest that the hardest-hit areas, such as NYC, have achieved TCI following the first wave of the epidemic, but likely remain below the long-term HIT. Thus, in contrast to some previous claims, these reqions can still experience subsequent waves. O_TEXTBOXSignificance Statement Epidemics generally spread through a succession of waves that reflect factors on multiple time-scales. Here, we develop a general approach to encompass super-spreading and population heterogeneity, and demonstrate that a fragile state of transient collective immunity (TCI) emerges well below the HIT during early, high-paced stages of the epidemic. However, this is not an indication of herd immunity: subsequent waves can and will emerge due to behavioral changes in the population, driven (e.g.) by seasonal factors. Analysis of empirical data suggests that even in locations with strong first waves of COVID-19, subsequent waves will still emerge. C_TEXTBOX