Anticipating and rapidly responding to respiratory virus outbreaks with continuous air sampling in K-12 schools

Project summary/abstract COVID-19 revealed weaknesses in respiratory pathogen surveillance. Tools to detect SARS-CoV-2 were initially limited. Now that SARS-CoV-2 is prevalent, schools and other congregate settings struggle to perform systematic surveillance. As influenza virus and other respiratory pathogens return to co-exist alongside SARS-CoV-2, pandemic fatigue undermines risk mitigation strategies that rely on behavior modification, creating an unprecedented risk of illness-caused absenteeism in schools. We hypothesize that frequent air sampling in K-12 schools is a simple, inexpensive, and accurate sur- veillance strategy for identifying when schools and the communities where they are located are at high risk for SARS-CoV-2 and influenza virus transmission. In this study, we will place an air sampler inside each of 16 K-12 schools and collect continuous air sam- ples twice weekly throughout the school year. We will test air samples for SARS-CoV-2 and influenza vi- rus, the two leading causes of respiratory virus absenteeism. A key question is whether detecting these viruses in air samples is non-inferior to comprehensive individual testing of people with influenza-like illnesses. We are partnering with the Oregon (Wisconsin) School District which already performs in- school rapid diagnostic testing of symptomatic individuals. Within this unique setting, we can determine if the detection rates of SARS-CoV-2 and influenza are similar between air surveillance and diagnostic testing of individuals. If they are similar, this would suggest that simple, inexpensive, and anonymous air sampling programs could be the foundation of school-based sentinel virus surveillance programs. We will also determine whether detecting and sequencing SARS-CoV-2 in air samples forecasts broader community disease spread. Using SARS-CoV-2 diagnostic testing, wastewater, environmental, and mo- bility data alongside in-school air sampler detection and sequencing data, we will develop and iterate a model using air sampler data to forecast community transmission. We will extend this model to forecast the risk for influenza viruses that are not currently surveilled systematically. Successfully developing precision air sampler surveillance for SARS-CoV-2 and influenza virus in a large public school setting would provide a model for a nationwide surveillance program. Such a program would lead to safer classrooms, improve awareness of respiratory viruses that threaten specific commu- nities, and increase resilience to respiratory pathogens that threaten schools in the future.