Helping Schools Calculate Respiratory Infection Risk

Amanda Wilson, PhD

The burden of respiratory viral diseases is greater than the approximate 6.6 million deaths and 633 million infections globally due to COVID-19. The U.S. has recently experienced an influenza epidemic,¹ and high respiratory syncytial virus (RSV) case rates.²

With a high number of students and staff together in close quarters, school children are extremely susceptible to not only getting a respiratory infection, but also to passing it on to others. School health personnel are important advocates for reducing viral transmission in classrooms.³

Our preliminary data from the COVID-19 pandemic show that school health personnel lack support to implement intervention guidance during outbreaks, and classrooms struggle to implement broad guidance due to instructional challenges.^4–6 Quantitative microbial risk assessments (QMRA)⁷ can be used to address this gap in school health resources and further provide school personnel with necessary decision making tools.

QMRAs involve the use of information such as mathematical representations of pathogens spread, pathogen dose-response relationships, and human behaviors in specific situations to estimate a probability of a negative health outcome, usually infection, given a specific exposure level. These QMRA models can serve as fast and inexpensive decision-making tools during outbreaks or future pandemics. However, these risk assessment models tend to be in the form of mathematical equations or computational code which are difficult for the lay person to use. However, there are recent efforts in the field of QMRA^8–11 to put these models in the form of applications or user-friendly tools to increase accessibility to decision-makers.

Our American Lung Association Catalyst Award will serve 3-fold:

• Develop risk assessment models to estimate respiratory viral infection risk reductions for bundled interventions in elementary school environments.
• Create and pilot a risk decision tool for school health personnel for informing intervention strategies.
• Evaluate a range of plausible intervention effect sizes to support the development of the risk calculator tool for a future intervention study.

We have developed a prototype QMRA risk calculator tool that allows users to explore hypothetical classroom scenarios to estimate risk of infection per student. User inputs include number of students, student grade-level, activity level of students, room size, fresh air exchange rate, percent of students assumed to be infected, pathogen of interest (i.e., rhinovirus, influenza A virus, SARS-CoV-2), and more.

We recently distributed an online survey to school administrators, teachers, school health staff and facilities mangers at three public school districts, 3 private schools, and the Arizona Department of Education’s Arizona School Nurse Access Program (ASAP). This survey will gather information about strategies and barriers to implementing interventions for reducing transmission during flu season or respiratory viral outbreaks.

Data from the survey will be used to iteratively improve our risk assessment tool’s user inputs and to create some hypothetical case scenarios (e.g., rhinovirus infections among 5% of the class that spent 1 hour in a small music classroom with poor fresh air circulation) using the tool to predict how single and bundled interventions can reduce average individual student infection risks for given cases.

We are also soliciting feedback on the design, usability, and interpretability of our prototype tool. In June 2024, we presented the prototype tool at a workshop as part of the National Association for School Nurses Conference 2024. Attendees provided feedback on tool input options and brainstormed on potential uses of risk-based tools to support school health. Results are being analyzed, and we are scheduling focus groups with Arizona-based school districts to gain further feedback on the prototype tool. Other future efforts include measuring the tool’s ability to inform decision-making for school personnel. This will be done using a survey of 80 school health personnel before and after use of the tool.

Developing risk decision tools for school health personnel to reduce respiratory infection transmission will protect asthmatic children’s health and offer schools needed public health support. The generated data will support future research that expands this work to include 1) schools across other grade levels and from other geographic regions, 2) evaluation of household contributions to infection risks, 3) evaluation of tool effectiveness.

Wilson has worked on previous projects funded by companies that make air and surface hygiene products, including SC Johnson, Reckitt, and Ecolab.

References

1. ^{Prater E. The U.S. is officially in a flu epidemic, federal health officials say. They’re preparing to deploy troops and ventilators if necessary. Fortune Well. https://fortune.com/well/2022/11/04/us-united-states-in-flu-epidemic-federal-health-officials-say-cdc-hhs-rsv-covid-omicron-2022/. Published November 4, 2022. Accessed November 9, 2022.}
2. ^{McPhillips D. RSV hospitalizations shoot up to levels typically seen in December, CDC data shows. CNN Health. https://www.cnn.com/2022/11/03/health/rsv-hospitalizations-high-levels. Published November 3, 2022. Accessed November 9, 2022.}
3. ^{Centers for Disease Control and Prevention. Asthma in Children. Published online June 27, 2019. Accessed November 9, 2022. https://www.cdc.gov/healthyschools/bam/diseases/asthma-in-children.htm}
4. ^{Wilson AM, Ogunseye OO, DiGioia O, Gerald LB, Lowe AA. Barriers to COVID-19 Intervention Implementation in K-5 Classrooms: A Survey of Teachers from a District with Mask Mandates despite a Statewide Mask Mandate Ban. Int J Environ Res Public Health. 2022;19(14):8311. doi:10.3390/ijerph19148311}
5. ^{Lowe AA, Ravi P, Gerald LB, Wilson AM. The Changing Job of School Nurses during the COVID-19 Pandemic: A Media Content Analysis of Contributions to Stress. Ann Work Expo Health. Published online September 16, 2022:wxac053. doi:10.1093/annweh/wxac053}
6. ^{Wilson AM, Ravi P, Pargas NT, Gerald LB, Lowe AA. School health systems under strain: an example of COVID-19 experiences & burnout among school health staff in Pima County, Arizona. BMC Public Health. 2023;23(1):1626. doi:10.1186/s12889-023-16532-8}
7. ^{Haas CN, Rose JB, Gerba CP. Quantitative Microbial Risk Assessment: Second Edition. Wiley-Blackwell; 2014.}
8. ^{Gerrity D, Crank K, Steinle‐Darling E, Pecson BM. Establishing pathogen log reduction value targets for direct potable reuse in the United States. AWWA Water Sci. 2023;5(5):e1353. doi:10.1002/aws2.1353}
9. ^{Gerrity D, Kaufman A, Pecson B, Seto E, Olivieri A. DPRisk. Published online 2019. https://edmundseto.shinyapps.io/QMRAv100/}
10. ^{Heida A, Maal-Bared R, Veillette M, et al. Quantitative microbial risk assessment (QMRA) tool for modelling pathogen infection risk to wastewater treatment plant workers. Water Res. Published online May 2024:121858. doi:10.1016/j.watres.2024.121858}
11. ^{Wilson AM, Canter K, Abney SE, et al. An application for relating Legionella shower water monitoring results to estimated health outcomes. Water Res. 2022;221:118812. doi:10.1016/j.watres.2022.118812}
12. ^{Sepucha KR, Borkhoff CM, Lally J, et al. Establishing the effectiveness of patient decision aids: key constructs and measurement instruments. BMC Med Inform Decis Mak. 2013;13(S2):S12. doi:10.1186/1472-6947-13-S2-S12}