In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal “SHIELD: Target, Test, and Tell” program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 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.
Brown Dog is an extensible data cyberinfrastructure, that provides a set of extensible and distributed data conversion and metadata extraction services to enable access and search within unstructured, un-curated and inaccessible research data across different domains of sciences and social science, which ultimately aids in supporting reproducibility of results. We envision that Brown Dog, as a data cyberinfrastructure, is an essential service in a comprehensive cyberinfrastructure which includes data services, high performance computing services and more that would enable scholarly research in a variety of disciplines that today is not yet possible. Brown Dog focuses on four initial use cases, specifically, addressing the conversion and extraction needs in the research areas of ecology, civil and environmental engineering, library and information science, and use by the general public. In this paper, we describe an architecture that supports contribution of data transformation tools from users, and automatic deployment of the tools as Brown Dog services in diverse infrastructures such as cloud or high performance computing (HPC) based on user demands and load on the system. We also present results validating the performance of the initial implementation of Brown Dog.
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