Associate Professor
School of Public Health
Division of Biostatistics
University of California
Berkeley, CA 94720-7360
hubbard@stat.berkeley.edu
510-642-8365 Fax: 510-642-5815
 
Creative Achievements
• Bayesian inference applied to infectious diseases--using prior knowledge to gain efficiency
• Parameter estimation and uncertainty in mathematical models of disease--estimating parameters (e.g., rate of secondary transmission) from outbreak data and disease models
• Locally efficient estimation in censored-data models--using covariate information to get better estimates when data has many missing values
• Causal inference--trying to estimate the causal effect of risk factors from observational data
 
Current Research Interests
• Risk analysis for airborne pathogens
• Clustering of longitudinal functions (finding diagnostic groups from longitudinal data)
• Bayesian melding to parameters in infectious disease models
• Model selection using cross-validation
 
Key Publications
• Hubbard, A.E., Liang, S., Maszle, D., Qui,D., Gu, X., Spear, R.C. 2002. Estimating the distribution of worm burden and egg excretion of Schistosomiasis japonica by risk group in th Sichuan Province, China. In press at Parasitology.
• Spear, R.C. Hubbard, A.E., Liang, S. and Seto, E. 2002. Disease Transmission Models for Public Health Decision Making: Toward an Approach for Designing Intervention Strategies for Schistosomiasis japonica. Env. Health Persp 110(9): 907-915.
• Brookhart, M.A., Hubbard, A.E., van der Laan, Colford, J.M. and Eisenberg, J.N.S. 2002. Insight into an outbreak: statistical estimation of parameters in a disease transmission model. In press at Statistics in Medicine.
• Hubbard, A.E., van der Laan, M.J., Enanoria, W. and Colford, J. 2000. Nonparametric survival estimation when death is reported with delay. Lifetime Data Analysis 6: 237-250.
• Hubbard, A.E., van der Laan, M.J. and Robins, J.M. 1999. Nonparametric locally efficient estimation of the treatment specific survival distributions with right censored data and covariates in observational studies. In: Statistical Models in Epidemiology: The Environment and Clinical Trials. Halloran, E. and Berry, D., eds,. NY: Springer-Verlag, pp. 134-178.
 
Teaching
• Longitudinal Data Analysis
• Causal Inference
• Computational Biology
• Risk Research