The threat of COVID-19 and the coordinated policy responses playing out in real time around the globe are unprecedented. Evidence can help light the path forward. Together with our partners, Mathematica is applying our unique knowledge and experience at the intersection of data, analytics, policy, and practice to help address today’s complex challenges related to COVID-19. Mathematica, Comagine Health, and Allegis recently joined the Department of Health and other stakeholders to implement the Washington State COVID-19 Contact Tracing Partnership. In addition, the state of Connecticut engaged Mathematica to assess and improve its response to COVID-19 in long-term care facilities. And by working in partnership with OnPacePlus, Mathematica has implemented a workforce-readiness dashboard for employers to help ensure the safety and efficiency of their workforce. Here are some additional projects that help address the pandemic.
Getting back to school safely
University of California, San Diego, Return to Learn program
Informed decisions about how we can safely return to schools and college campuses require leading-edge, evidence-based approaches. Universities, in particular, face important decisions regarding conditions for reopening and strategies to detect and prevent outbreaks. We’re working closely with researchers at the University of California, San Diego (UCSD), on aspects of their Return to Learn program. The program encompasses an adaptive strategy of risk mitigation, viral monitoring, and public health intervention to detect COVID-19 outbreaks early and prevent their spread on campus.
Mathematica’s COVID-19 agent-based computational model (ABM) for educational institutions can simulate the campus at opening and throughout the year under hundreds of scenarios. Mathematica’s simulations allow UCSD to bring data-driven decision making to its campus reopening plan, informing decisions surrounding student housing density, in-person class structure, general campus-wide COVID-19 policy, and student testing frequency. The ABM will also support UCSD as students return to campus. The Return to Learn program will continuously monitor and integrate real-time data—including asymptomatic and symptomatic testing, wastewater analyses, proximity data, molecular data, survey data, contact-tracing data, and campus data (such as housing and class registration); the program monitoring also incorporates contextual information about geography, contact structure, behavior, and epidemiology. Mathematica’s ABM will evolve with the data stream from the wider Return to Learn effort, refining forecasts, answering new questions, and anticipating outbreaks.
K–12 guidance
In May 2020, the Pennsylvania Department of Education (PDE) approached the Regional Educational Laboratory (REL) Mid-Atlantic, led by Mathematica, for analytic support of its effort to produce guidance for reopening school buildings in the midst of the COVID-19 pandemic. REL Mid-Atlantic partnered with PDE on a three-part project, which included (1) examining emerging evidence on COVID-19’s public-health and educational implications for schools, (2) interviewing a wide range of Pennsylvania stakeholders to assess concerns and challenges related to reopening school buildings, and (3) modifying Mathematica’s COVID-19 ABM to assess likely disease spread among students and school staff under various approaches to reopening school buildings. Findings are available in a memo and serve as the foundation for a publicly available tool for exploring the spread of COVID-19 among students, faculty, and staff at K–12 schools under different approaches to school reopenings.
Using wastewater to detect outbreaks
Applying lessons from more than three years of work on the opioid epidemic and successfully tracking community-level wastewater measures, Mathematica is working to develop insights for COVID-19 pandemic management. With more than 15,000 wastewater treatment plants around the country already collecting samples to measure environmental pollutants, wastewater surveillance holds promise for efficiently conducting rapid, repeated, community-wide COVID-19 testing using infrastructure that many municipalities already have in place.
To validate our approach to translating wastewater data for pandemic management, we recently completed a wastewater pilot study to assess COVID-19 exposure in a rural North Carolina community that is home to a major university population. In partnership with the Tuckaseigee Water and Sewer Authority, Jackson County Department of Public Health, and the University of Wisconsin’s School of Freshwater Sciences, we examined how trends in SARS-CoV-2 viral levels measured in wastewater aligned with trends in confirmed COVID-19 case counts and a proxy measure based on doctor visits and COVID-like symptom reports. To contextualize the wastewater data for public health officials, Mathematica built a generalizable dynamic wastewater dashboard. The dashboard brings together wastewater data with community data on numbers of tests conducted, confirmed cases, hospitalizations, and deaths; Jackson County’s pandemic vulnerability; changes in population mobility; and the prevalence of risk factors for severe COVID-19 presentation. Our results revealed strong trend alignment between the data sources over the four-week sampling period. Moreover, the study confirms findings from Yale University researchers that wastewater data can serve as a leading indicator for changes in COVID-19 risk—the wastewater data provided a lead time of eight to nine days for changes in SARS-CoV-2 viral levels compared to confirmed case counts or proxy indicators.
