ArticleInternational journal of environmental research and public health2025
Assessing Public Health Capacity for Infectious Disease Modeling: A Qualitative Study of State and Local Agencies.
Article in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advancing epidemic intelligence: evaluating Senegal's mpox surveillance system and readiness for AI-driven predictive modelling.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Infectious disease modeling and forecasting tools are crucial for outbreak management. However, variability exists in the capacity of state and local health departments to effectively utilize these tools, influenced by factors such as infrastructure, funding, staff capacity, and data access. This study aims to identify the current priorities, needs, and capacities of state and local public health departments regarding infectious disease modeling and forecasting tools. Key informant interviews were conducted with epidemiologists, informaticists, and leadership across state and local health departments from Montana, Utah, and Washington. Thematic coding and axial coding were used for thematic analysis. Three themes emerged: (1) models and tools must be adaptable based on the jurisdiction type (rural, urban, state); (2) building trust in models and tools is an important precursor to adoption; and (3) there are concerns about the availability and quality of data. This study highlights the need for adaptable modeling tools that are tailored to specific public health jurisdictions. Building trust in modeling and forecasting tools and addressing data quality issues are essential for successful tool implementation and adoption across diverse public health settings.
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Registered trials
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