Background The COVID-19 pandemic has highlighted the critical and ongoing need for leaders across health care, public health, and government to have real-time, hyper-local data. These data need to be relevant and meaningful across multiple sectors. Moreover, data are most likely to be useful when they facilitate connections across systems, enable situational awareness, and drive equitable decision making.
Help symposium participants utilize currently available COVID-19 data to provide system-level situational awareness (e.g., city/county) and to predict potential future states of the pandemic
Share themes emergent from stakeholders across a diverse set of US communities regarding their data systems and decision-making processes
Identify practical applications of data and tools in participants’ localities
Methods In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers of change were explored through a series of nine 45-minute interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future public health practices, tools, and systems for supporting equitable population health decision making.
Results Interview responses fell into four themes that contribute to effective, equitable, community driven responses to COVID-19: real-time, accessible data that is mindful of the tension between community transparency and individual privacy; fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment. These themes aligned with our preliminary drivers of change and provided additional insights into their application.
Conclusions There was broad agreement amongst public health leaders and community leaders about the key elements of the data and learning systems required to manage current and future public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises (figure 1).
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