Rocky Mountain West COVID-19 Modeling: A descriptive assessment of the current public health needs during the COVID-19 pandemic to endemic transition

Abstract

Abstract Background: Mathematical models of infectious diseases have played a key role in the response to the COVID-19 pandemic and many prior infectious disease outbreaks. Effective models require understanding decision-makers' needs. While much attention was placed on the needs of public health leaders in the early phase of the pandemic, by summer 2022, the way needs evolved was poorly documented. Moreover, needs in rural areas have never been well-understood. Getting a more complete and up-to-date description of these needs is key to designing modeling and data visualization tools that can aid in public health response. Methods: We conducted semi-structured interviews with public health leaders representing state, local, and tribal health departments in the Rocky Mountain West in the summer of 2022 to understand their priorities, decision-making needs, and data resources over two years into the COVID-19 pandemic. Results: Public health leaders from six organizations representing three states, local, regional, and tribal public health agencies in the Rocky Mountain West participated in our interviews. Responses to priorities and needs covered a broad range of topics. Priorities included having timely information on vaccines, anticipating COVID-19 demands on hospitals, understanding the potential impacts of emerging variants and communicating immunity concepts to the public. Decisions focused on making public health recommendations as opposed to mandates. Travel-related introductions of SARS-CoV-2 infections were not a present concern compared to earlier in the pandemic. Limited access to timely COVID-19 data and challenges tracking COVID-19 hospitalizations was a challenge mentioned by multiple interviewees. Conclusions: Over two years since SARS-CoV-2 emerged, there is still an important role for modeling during epidemic and pandemic responses. There remain major gaps in data availability, particularly related to COVID-19 hospital demand, that make it challenging to utilize timely and granular data to inform and parameterize models.