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The Mathematical Modeling Informing Return to Learn

UC San Diego’s Return to Learn Program, launched in May 2020, incorporates three interdependent pillars to reduce the risk of SARS-CoV-2 on campus:

  • Risk mitigation strategies include masking, social distancing, sanitation and ventilation, along with structural interventions such as reducing density of individuals in research and residential campus buildings as well as offering hybrid and remote class instruction, with limits to class size.
  • Viral detection strategies incorporate symptomatic and asymptomatic testing along with other measures to detect outbreaks early such as wastewater and other environmental (e.g. surfaces and air filter) monitoring.
  • Public health intervention includes traditional case notification, isolation, contact tracing and quarantine activities, along with digital exposure notification technologies.

The Return to Learn strategy is multi-pronged and adaptive, founded on a data-driven quantitative framework. This data guides the program and informs campus decisions on the relative benefits of the risk mitigation, viral detection and public health intervention strategies developed at UC San Diego. To do this, we created an agent-based network model of SARS-CoV-2 transmission among the university population (students, faculty and staff). These computational models imitate how interactions of individuals (“agents”) contribute to community-level outcomes. Our models include interactions within the classroom and on- and off-campus residences, as well as interactions on campus outside of classrooms and residences and import of infections from outside the university.

These models help us investigate the impact of multiple strategies, implemented in isolation and combined:

  • Campus housing de-densification
  • Classroom maximum capacity caps
  • Hybrid instruction
  • Asymptomatic testing with various test sensitivities
  • Masking and physical distancing
  • Isolation, contact tracing and quarantine

Model Findings

The Return to Learn program—and modeling that shapes it—is driven by an adaptive strategy. We are continually collecting data, refining our understanding of the situation and associated modeling, and modifying tactics accordingly to significantly reduce the risk of transmission of SARS-CoV-2. We encourage you to return here for updates to the model and analyses as we continue this iterative process. To learn more, please see the preprint version: “Evaluation of SARS-CoV-2 transmission mitigation strategies on a university campus using an agent-based network model.”

Explore interactive models

On the Mathematica website, you may investigate the impact of adherence to masking and social distancing, testing frequency and structural interventions at UC San Diego through simulation modules. Delve into the interactive modules by clicking on the image below.


SARS-CoV-2 Model