School Masking Policies and Secondary SARS-CoV-2 Transmission.

OBJECTIVES: Throughout the COVID-19 pandemic, masking has been a widely used mitigation practice in kindergarten through 12th grade (K-12) school districts to limit within-school transmission. Prior studies attempting to quantify the impact of masking have assessed total cases within schools; however, the metric that more optimally defines effectiveness of mitigation practices is within-school transmission, or secondary cases. We estimated the impact of various masking practices on secondary transmission in a cohort of K-12 schools. METHODS: We performed a multistate, prospective, observational, open cohort study from July 26, 2021 to December 13, 2021. Districts reported mitigation practices and weekly infection data. Districts that were able to perform contact tracing and adjudicate primary and secondary infections were eligible for inclusion. To estimate the impact of masking on secondary transmission, we used a quasi-Poisson regression model. RESULTS: A total of 1 112 899 students and 157 069 staff attended 61 K-12 districts across 9 states that met inclusion criteria. The districts reported 40 601 primary and 3085 secondary infections. Six districts had optional masking policies, 9 had partial masking policies, and 46 had universal masking. In unadjusted analysis, districts that optionally masked throughout the study period had 3.6 times the rate of secondary transmission as universally masked districts; and for every 100 community-acquired cases, universally masked districts had 7.3 predicted secondary infections, whereas optionally masked districts had 26.4. CONCLUSIONS: Secondary transmission across the cohort was modest (<10% of total infections) and universal masking was associated with reduced secondary transmission compared with optional masking.

Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening

IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening and maintenance of in-person learning. OBJECTIVES/GOALS: School closures resulting from the COVID-19 pandemic disrupt student education and health and exacerbate inequities. Public health agencies and school districts currently lack pragmatic models to assess the effects of potential strategies for resuming and maintaining in-person learning on outcomes such as transmission and attendance. METHODS/STUDY POPULATION: This study explored how various combinations of transmission-mitigating interventions affect health and learning outcomes in a range of underlying epidemiological conditions. The CTSA science team developed a conceptual framework and an agent-based simulation model with parameters including prevalence, transmission, testing, preventive and responsive actions, infection control, population behavior and awareness, and the potential impact of vaccine adoption and exemption policies. The team partnered with a large school district to ensure relevance of the program components to decision-making. RESULTS/ANTICIPATED RESULTS: The model shows that no single program element or condition ensures safety. Combining interventions can result in synergy in the mitigation efforts. Even without testing, an efficient health screening process with forthcoming risk reporting, combined with on-campus infection control, can reduce on-campus transmission. The resulting model is accessible online to enable exploration of likely scenarios. It is adaptable as COVID-19 science evolves, including for testing and vaccines. DISCUSSION/SIGNIFICANCE OF FINDINGS: This research provides public health agencies and school districts with a model that couples local conditions with programmatic elements to help inform the local COVID-19 response, recognizing that decisions about the school community are often complex politically, technically, and operationally when it comes to addressing a health crisis.

Using Control Charts to Inform Public Health and Community Engagement during the COVID-19 Pandemic

IMPACT: Demonstrate applicability of an underutilized method for showing variation that enables public health agencies to respond to the COVID-19 pandemic OBJECTIVES/GOALS: Enacting sensible public policies in the coronavirus disease 2019 (COVID-19) pandemic requires real-time data that civic and public health leaders can easily interpret and act on. This collaboration between a CTSA and a local health department sought a novel use of control charts to provide timely and interpretable data. METHODS/STUDY POPULATION: Healthcare and other industries use control charts to understand the behavior of processes and systems so they can intervene on them. The CTSA science team developed statistical process control charts at the neighborhood level to help illustrate their value for decision-making as the pandemic progresses. This method included accounting for congregate populations (skilled nursing facilities, correctional facilities) to produce data for the general public. RESULTS/ANTICIPATED RESULTS: Patterns in COVID-19 vary over time by neighborhood. Juxtaposing control charts with social characteristics of local areas in a dashboard format provides granularity for decision-makers and data for engaging communities in changing behavior. Annotating time series charts in real time connects events and local knowledge with observed data, which can help authorities and people to learn and act based on variations displayed by the control charts about disease outbreaks and cases. School districts are among those that could benefit from control charts with information about the school community and how COVID-19 spread is occurring. DISCUSSION/SIGNIFICANCE OF FINDINGS: Control charts have rarely been used in public health despite their ease of use and interpretability. This study demonstrates a novel approach to providing timely, accurate data that can support real-time decision-making of government and public health as well as school districts, businesses, and others.

CTSA collaboration to support K-12 school re-opening in the COVID-19 pandemic

IMPACT: The mobilization of a CTSA-sponsored team with multi-disciplinary translational science expertise enabled the university to provide a range of T1-T4 expertise to a large, complex school district that resulted in permanent learning and data science infrastructure. OBJECTIVES/GOALS: The Clinical Translational Science Institute (CTSI) formed a multidisciplinary science team to provide expertise in support of the re-opening of in-person learning in the second-largest U.S. school district during the COVID-19 pandemic. METHODS/STUDY POPULATION: The assembled interdisciplinary science team provided expertise in epidemiology, machine learning, causal inference and agent-based modeling, data and improvement science, biostatistics, clinical and laboratory medicine, health education, community engagement, and experience in outbreak investigation and management. The team included TL1 pre and postdoctoral fellows and mobilized scientists from multiple professional schools and T1-T4 stages of translational research. RESULTS/ANTICIPATED RESULTS: Tangible outcomes achieved using this team approach included the development of practical metrics for use in the school community, a learning process, the integration of preventive design elements into a testing and tracing program, and targeted and data-driven health education. The team, for example, generated new data displays for community engagement and collaborated with the school district in their use to visualize, learn from, and act on variation across a 700 square mile region. DISCUSSION/SIGNIFICANCE OF FINDINGS: Novel translational methods can be used to establish a learning environment and data science infrastructure that complements efforts of public health agencies to aid schools in the COVID-19 pandemic. These new capabilities apply to COVID-19 testing and vaccines and can be mobilized for future population health challenges faced by school districts.

Using control charts to understand community variation in COVID-19.

Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses. Our aim was to demonstrate a novel use of statistical process control to provide timely and interpretable displays of COVID-19 data that inform local mitigation and containment strategies. Healthcare and other industries use statistical process control to study variation and disaggregate data for purposes of understanding behavior of processes and systems and intervening on them. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. Such disaggregation provides granularity that decision-makers can use to respond to the pandemic. The annotated time series presentation connects events and policies with observed data that may help mobilize and direct the actions of residents and other stakeholders. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. Control charts could prove valuable given their potential ease of use and interpretability in real-time decision-making and for communication about the pandemic at a meaningful level for communities.