Understanding and Mitigating Airborne Transmission
Flu Lab commissioned Freedman Consulting, LLC to examine ways to advance new measures to make indoor spaces less permissive to airborne infectious disease transmission. We are pleased to share the results of their effort.
The Center for Green Schools has published an indoor air quality fact sheet series to equip school systems with information needed to develop effective strategies for the reduction of airborne virus transmission in classrooms. Many transmission reduction measures, including ventilation, filtration, and germicidal UV, will be effective in other settings, including workplaces, restaurants and homes.
The University of Maryland Public Health Aerobiology, Virology, and Exhaled Biomarker Lab is a significant contributor to public knowledge about infectious aerosols, particularly regarding COVID-19. PHAB Lab recently published research on the superiority of saliva sample collection and the evolution of SARS-CoV-2 toward increased transmissibility, measured through exhaled breath.
The Shoo the Flu school-located influenza vaccination model, developed and piloted in the Bay Area, increased influenza vaccination among students, decreased illness-specific school absences, and lower influenza transmission community-wide. More evaluation information is available in this open source research article, and the Shoo the Flu model is ready for replication with this Toolkit.
The Behavior Change for Good Initiative identified the top-performing interventions to increase influenza vaccination rates in adults. Their two studies, the largest ever research studies aimed at increasing vaccine adoption, show a cost-effective way to encourage vaccination: sending SMS reminders mentioning a flu vaccine is “reserved” or “waiting for you.”
Photo courtesy of the Vaccines Saves Lives Project
Early Signals and Surveillance
Stanford Healthcare Innovation Lab built a real-time, smartwatch-based alerting system that detects signals associated with the onset of early infection. This system can predict the onset of illness, including for COVID-19, before symptoms, which provides actionable information to users.
Outbreaks Near Me is a participatory disease surveillance system using anonymous self reports of symptoms to create a map of disease outbreaks in your area. This crowdsourced system also helps track national trends, including the use of at-home testing. You can join the Outbreaks Near Me community here.