Background/Purpose: Local health departments are increasingly expected to make rapid, data-informed decisions, yet adoption of artificial intelligence (AI) tools remains limited due to workforce training gaps and technical barriers. This study examined whether structured AI education improves public health professionals’ confidence and readiness to use no-code forecasting tools. The research addressed two questions: (1) How does AI-focused training influence knowledge and attitudes toward AI in disease forecasting? and (2) Can a no-code platform (Akkio) generate feasible county-level influenza forecasts using weather variables?
Methods: A quantitative pre–post design was implemented with public health professionals from Douglas and Jefferson Counties, Colorado (n=8). Participants completed a 20-item Likert-scale survey assessing AI literacy, readiness, and trust before and after two structured training sessions. During the sessions, attendees applied historical influenza and weather data within the Akkio no-code AI platform to generate 90-day forecasts. Paired t-tests were used to evaluate changes in survey responses, and forecasting outputs were reviewed for practical interpretability.
Results: Post-training scores increased across all survey domains. Familiarity with AI-driven forecasting tools increased from a mean of 1.25 (strongly disagree) to 3.75 (agree). Participants reported greater confidence in interpreting AI-generated forecasts and stronger agreement that public health professionals should receive AI training. Forecasting outputs demonstrated feasible short-term influenza projections using temperature-based variables, with higher predicted case trends observed in the 65+ population.
Implications: No-code AI platforms can reduce technical barriers while enhancing workforce readiness for predictive surveillance. Structured AI education improves confidence and supports responsible integration of forecasting tools into routine public health practice. Expanding AI literacy initiatives may strengthen local preparedness and data-driven decision-making nationwide.
