Key research themes
1. How does selection bias influence observational epidemiological studies during infectious disease outbreaks?
This theme investigates the impact and mechanisms of selection bias on estimations of infection risk and disease outcomes using observational data, especially in emergent epidemics like COVID-19 and Ebola. It addresses challenges inherent in preferential sampling of severe cases, non-random participation, and data linkage processes affecting the validity and generalizability of epidemiologic conclusions.
2. How can unmeasured confounding and systematic biases in observational epidemiological studies be quantitatively identified and addressed to improve causal inference?
This theme focuses on methodological advances in detecting, assessing, and mitigating the impact of unmeasured confounding and other systematic biases such as selection and information bias in observational epidemiology. It encompasses frameworks, bias analysis techniques, and guidelines developed to enhance the rigor of causal conclusions drawn from non-experimental data, particularly when randomized controlled trials are infeasible.
3. What are the effects and implications of biases in personal risk perception and epidemiological modeling for public health communication and decision-making?
This theme explores how biases in personal risk assessment and the pluralistic modeling approaches in epidemiology impact understanding, communication, and policymaking. It covers psychological biases such as optimistic risk misestimation, present bias influencing health behaviors, and epistemic considerations around the diversity and interpretation of epidemiological models with relevance for population health strategies.