Design and Implementation of an Innovative, Rapid Data-Monitoring Strategy for Public Health Emergencies: Pilot of...

During the COVID-19 pandemic, an urgent need existed for near-real-time data collection to better understand how individual beliefs and behaviors, state and local policies, and organizational practices influenced health outcomes. We describe the processes, methods, and lessons learned during the development and pilot testing of an innovative rapid data collection process we developed to inform decision-making during the COVID-19 public health emergency. We used a fully integrated mixed-methods approach to develop a structured process for triangulating quantitative and qualitative data from traditional (cross-sectional surveys, focus groups) and nontraditional (social media listening) sources. Respondents included students, parents, teachers, and key school personnel (eg, nurses, administrators, mental health providers). During the pilot phase (February-June 2021), data from 12 cross-sectional and sector-based surveys (n = 20 302 participants), 28 crowdsourced surveys (n = 26 820 participants), 10 focus groups (n = 64 participants), and 11 social media platforms (n = 432 754 503 responses) were triangulated with other data to support COVID-19 mitigation in schools. We disseminated findings through internal dashboards, triangulation reports, and policy briefs. This pilot demonstrated that triangulating traditional and nontraditional data sources can provide rapid data about barriers and facilitators to mitigation implementation during an evolving public health emergency. Such a rapid feedback and continuous improvement model can be tailored to strengthen response efforts. This approach emphasizes the value of nimble data modernization efforts to respond in real time to public health emergencies.