We aimed to visualize CDC data to identify trends in respiratory illnesses—COVID-19, influenza, and RSV—across sex, age, and racial demographics, while advancing concepts of data-driven public health interventions.

We conducted a demographic analysis of CDC emergency department surveillance data (July 2023-June 2024) using Python-based visualizations developed collaboratively with generative AI. Our approach stratified COVID-19, influenza, and RSV visit percentages by sex, age group, and race/ethnicity to identify temporal patterns and high-risk populations across respiratory conditions.

Analysis indicated no significant sex differences in visit rates, although females showed slightly higher rates for COVID-19, while males had higher rates for RSV. Age trends highlighted the highest visit rates for COVID-19 among young children (0–4) and the elderly (65+), influenza predominantly affecting children (0–4 and 5–17), and RSV primarily occurring in very young children (0–4). Racial disparities were observed, with Asian individuals exhibiting higher COVID-19 visit rates, while Hispanic individuals reported elevated rates for influenza and RSV.

Findings underscore the need for targeted public health interventions tailored to demographic-specific vulnerabilities, particularly for high-risk age groups and racial/ethnic communities disproportionately impacted by respiratory illnesses. Recommendations include culturally appropriate vaccination campaigns for Hispanic communities and specialized respiratory care for young children. This illustrates the potential of generative AI in enhancing analytical capabilities within public health, enabling a more nuanced understanding of respiratory illness trends and informing equitable health strategies that address systemic disparities across diverse populations. By integrating advanced data analytics, public health efforts can be more effectively aligned with demographic needs, ultimately working toward a healthier and more equitable society.