Do No Harm - Terminology, Assumptions, and Interpretation of Epidemic Models in Communities Most Affected

Explore a thought-provoking colloquium on ethical considerations in epidemic modeling, focusing on HIV and COVID-19. Examine how terminology, assumptions, and interpretations can impact communities disproportionately affected by these diseases. Learn about the evolution of HIV modeling practices and the importance of partnering with affected communities. Analyze common analytic biases, simplifying assumptions, and potential misinterpretations in epidemic models. Discover best practices for de-stigmatizing language in health research and gain insights from discussions with communities about HIV and COVID-19 modeling results. Reflect on the potential impact of models on inequitable public health responses and consider ways to improve future approaches.

Introduction
Presentation
Heterogeneity
Sources of heterogeneity
Cities
Equity and social justice
Fundamental insights
Access to testing
Contact rates
Vaccine uptake
Public health messaging
Bivariate analysis
Framing individuals as vectors
No longer social inequalities
Stigma
Political correctness
Consequences
Principles of practice
The importance of amplifying
Ed Young
Dr John OBrien
Thank you
Comments
Simple but not too simple
Model specification
Mathematical modeling
Field Institute
Open Access
Lab Website
Mathematical background
Mathematical epidemiology
Conclusion