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Harrison Quick, PhD

Harrison Quick

Assistant Professor of Biostatistics
Epidemiology and Biostatistics
hsq23@drexel.edu
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Degrees

PhD in Biostatistics, University of Minnesota

Bio

Dr. Harrison Quick joined the Dornsife School of Public Health as an Assistant Professor of Biostatistics in 2016. Prior to joining Drexel, Dr. Quick served as a postdoctoral researcher at the University of Missouri and as a senior service fellow at the Centers for Disease Control and Prevention.

From a methodological perspective, Dr. Quick is a Bayesian statistician whose primary areas of research include spatial statistics and data privacy.  His research in spatial statistics has been supported by the County Health Rankings & Roadmaps program and the Centers for Disease Control and Prevention, and he was recently awarded an R01 from the National Heart, Lung, and Blood Institute of the NIH to study spatiotemporal trends in heart disease mortality and its risk factors in Philadelphia and develop statistical tools for state and local health departments.  In addition, Dr. Quick served as an ASA/NCHS Research Fellow to conduct research related to data privacy at the National Center for Health Statistics and received an NSF CAREER award to pursue research at the intersection of spatial statistics and data privacy with an eye toward increasing access to public health data.

Collaboratively, Dr. Quick works with faculty from across the Dornsife School of Public Health and with colleagues at the Centers for Disease Control and Prevention. While most of his collaborative research pertains to analyses of spatial and spatiotemporal data, Dr. Quick also has a wealth of experience collaborating on projects plagued by missing and censored data.

Research Interests

    • Bayesian Inference
    • Spatial Analysis or GIS
    • Data Confidentiality
    • Statistical Modeling
    • Urban Health
    • Occupational Health

    Publications

    Quick, H. (2022). “Improving the utility of Poisson-distributed, differentially private synthetic data via prior predictive truncation with an application to CDC WONDER.” Journal of Survey Statistics and Methodology, 10, 596-617.

    Quick, H. (2021). "Generating Poisson-distributed differentially private synthetic data" J. Roy. Statist. Soc., Ser. A (Statistics in Society), 184, 1093-1108.

    Quick, H., Song, G., and Tabb, L.P. (2021). "Evaluating the informativeness of the Besag-York-Mollie CAR model." Spatial Spatio-temporal Epidemiol., 37, 100420.

    Quick, H., Terloyeva, D., Wu, Y., Moore, K., and Diez Roux, A.V. (2020). "Trends in tract-level prevalence of obesity in Philadelphia by race, space, and time." Epidemiology, 31, 15-21

    Quick, H. and Waller, L.A. (2018). "Using spatiotemporal models to generate synthetic data for public use." Spatial Spatio-temporal Epidemiol., 27, 37-45.

    Quick, H., Holan, S.H., and Wikle, C.K. (2018). “Generating partially synthetic geocoded public use data with decreased disclosure risk using differential smoothing.” J. Roy. Statist. Soc., Ser. A (Statistics in Society), 181, 649-661.

    Quick, H., Waller, L.A., and Casper, M. (2018). “A multivariate space-time model for analyzing county-level heart disease death rates by race and sex.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 67, 291-304.

    Quick, H., Waller, L.A., and Casper, M. (2017). “Multivariate spatiotemporal modeling of age-specific stroke mortality.” Ann. Appl. Stat, 11, 2170-2182.

    Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015). “Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography.” Spatial Statistics, 14, 439-451.

    Quick, H., Carlin, B.P., and Banerjee, S. (2015).  “Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 64, 799-813.

    Quick, H., Holan, S.H., and Wikle, C.K. (2015). “Zeros and ones: A case for suppressing zeros in sensitive count data with an application to stroke mortality.” Stat, 4, 227-234.

    Quick, H., Banerjee, S., and Carlin, B.P. (2015). “Bayesian modeling and analysis for gradients in spatiotemporal processes.” Biometrics, 71, 575-584.

    Visit Dr. Quick's personal website