Raffaele Vardavas

Raffaele Vardavas is a Professor of Policy Analysis at the RAND School of Public Policy and a senior-level mathematician at the RAND Corporation. His work integrates advanced mathematical modeling with public health policy, focusing on infectious disease transmission, socioeconomic systems, and behavioral dynamics.

• Institution: RAND Corporation
• School: RAND School of Public Policy
• Academic Rank: Professor
• Email: Raffaele_Vardavas@rand.org

He holds a Ph.D. and M.Sci. in Physics from Imperial College London and completed postdoctoral training in applied mathematics at UCLA. His research bridges epidemiology, economics, and computational modeling to inform real-world policy decisions.

Vardavas specializes in agent-based and population-level models for infectious diseases, with a focus on behavioral adaptation, social networks, and policy evaluation. His work spans influenza, HIV, antimicrobial resistance, and the socioeconomic impacts of pandemics like COVID-19. He has led numerous federally funded projects under NSF and NIH grants and contributed to WHO advisory efforts on integrated epidemiological-economic models.

The recent articles reflect a strong trend toward policy-relevant simulation modeling, particularly in pandemic response, health care financing, workforce dynamics, and climate-health interactions. His methodologies emphasize robustness, behavioral realism, and decision support under uncertainty.

Scientific Awards and Recognition:

• Principal Investigator on NSF and NIH (R21, R01) grants
• Member of WHO Technical Advisory Group on integrated models for COVID-19
• Reviewer for NIAID influenza and COVID-19 grant panels

Vardavas has led major research initiatives involving grant-funded projects, policy tools (e.g., the COVID-19 Decision Support Tool), and stakeholder-engaged modeling. His work has informed federal and state-level health policies, particularly in pandemic preparedness, insurance reform, and workforce planning. He is affiliated with interdisciplinary teams at RAND that combine behavioral science, economics, and systems modeling.

He is actively engaged in developing synthetic data methods, microsimulation models (e.g., LHIEM, COMPARE), and AI-driven approaches to public policy challenges. His lab-like environment at RAND supports large-scale computational modeling and cross-sector collaboration.