Charmaine Dean

V.P. University Research
Charmaine Dean

Contact Information:
Charmaine Dean

Role & Research Interests

Charmaine Dean is Vice-President, Research and Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research interest lies in the development of methodology for disease mapping, longitudinal studies, the design of clinical trials, and spatio-temporal analyses. Much of this work has been motivated by direct applications to important practical problems in biostatistics and ecology. Her current main research applications are in survival after coronary artery bypass surgery, mapping disease and mortality rates, forest ecology, fire management, smoke exposure estimation from satellite imagery, and modeling of temporary and intermittent stream flow for flood analysis and predictions.

Education/Biography

Dr. Dean received her Ph.D. degree from the University of Waterloo in 1988. She was 2007 President of the Statistical Society of Canada, 2002 President of the International Biometrics Society, Western North American Region, and has served as President of the Biostatistics Section of the Statistical Society of Canada. She has given eleven years of service to the Natural Sciences and Engineering Research Council of Canada, including two as Chair of the Statistical Sciences Grant Selection Committee and one as Chair of the Discovery Accelerator Supplement Committee for the Mathematical and Physical Sciences. She has served as Chair of the NIH Biostatistics Grant Review Panel; on the Michael Smith Foundation for Health Research Advisory Council and on selection panels for that foundation; on the Board of Directors of the Pacific Institute for the Mathematical Sciences; on the Scientific Advisory Board of the Banff International Research Station; and as a member of the College of Reviewers of the UK Engineering and Physical Sciences Research Council. She is a member of the Mitacs College of Reviewers and of College of Reviews of the Canada Research Chairs Program. She is Associate Editor of Biometrics, of Environmetrics, and of Statistics in Biosciences, and Senior Editor of Spatial and Spatio-temporal Epidemiology.

From 2011 to 2017, Charmaine Dean served as Dean of Science at Western University.  In her role as Dean, she provided leadership and oversight for all faculty, staff, students and operations for the Faculty of Science as well as in University matters and key relationships outside the University. Prior to her service at Western, she played a major role in establishing the Faculty of Health Sciences at Simon Fraser University in her capacity of Associate Dean of that Faculty. Previously, she was the founding Chair of the Department of Statistics and Actuarial Science at Simon Fraser University.

Awards & Achievements

In 2003, Dr. Dean was awarded the CRM-SSC prize; she was named Fellow of the American Statistical Association (2007), Fellow of the American Association for the Advancement of Science (2010), Elected Member of the International Statistical Institute (2016), Fellow of the Institute of Mathematical Statistics (2020), Fellow of the Fields Institute for Research in Mathematical Sciences (2022); in 2007 awarded the University of Waterloo Alumni Achievement Medal; in 2012 awarded the Trinidad & Tobago Canadian High Commission Award; in 2023 awarded the Statistical Society of Canada Gold Medal; and in 2024 awarded the L’Ordre des Palmes Académiques (Chevalier) by the Government of France.

Selected Publications

  • Peng, K.K., Renouf, E.M., Dean, C.B., Hu, X.J., Delatolla, R., Manuel, D.G., (2023), An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada. Infectious Disease Modelling, In Press. doi: 10.1016/j.idm.2023.05.011

  • Bucyibaruta, Georges, Dean, C.B., and Torabi, Mahmoud, (2023), A discrete-time susceptible-infectious-recovered-susceptible model for the analysis of influenza data. Infectious Disease Modelling, 8(2), 471-483. doi: 10.1016/j.idm.2023.04.008

  • Kaida, A., Anderson, J. Barnard, C., Batram., L., Bert, D., Carpendale, S., Dean, C., ..., Smith, M., (2023), Realizing the Promise of Disaggregated Data and Analytics for Social Justice Through Community Engagement and Intersectoral Research Partnerships, Engaged Scholar Journal: Community-Engaged Research, Teaching, and Learning, 8(4),57-71, doi: 10.15402/esj.v8i4.70792.

  • Granville, Kevin, Woolford, D.G., Dean, C.B., Boychuk, D. and McFayden, C.B., (2022), On the Selection of an Interpolation Method with an Application to the Fire Weather Index in Ontario, Canada, Environmetrics, doi: 10.1002/env.2758.
  • Becker, Devan G., Woolford, D.G. and Dean, C.B., (2022), Assessing dependence between frequency and severity through shared random effects with application to wildland fires in British Columbia, Canada, PLOS One, doi: 10.1371/journal.pone.0271904.
  • Dean, C.B., El-Shaarawi, A.H., Esterby, S.R., Mills Flemming, J., Routledge, R.D., Taylor, S.W., Woolford, D.G., Zidek, J.V., Zwiers, F.W., (2022), Canadian Contributions to Environmetrics, Canadian Journal of Statistics, 50 (4), doi: 10.1002/cjs.11743.
  • Granville, Kevin, Woolford, D.G., Dean, C.B. and McFayden, C.B., (2022), A Case- Crossover Study of the Impact of the Modifying Industrial Operations Protocol on the Frequency of Industrial Forestry-Caused Wildland Fires in Ontario, Canada, Journal of Agricultural, Biological and Environmental Statistics, doi: 10.1007/s13253-022-00497- z.
  • Granville, Kevin, Woolford, D.G., Dean, C.B. and McFayden, C.B., (2022), Wildland fire prevention: the impact of the Modifying Industrial Operations Protocol on the growth of industrial forestry-caused wildland fires in Ontario, Canada., International Journal of Wildland Fire.
  • Lundy, E., and Dean, C.B. (2021), Analyzing Heaped Counts Versus Longitudinal Presence/Absence Data in Joint Zero-inflated Discrete Regression Models, Sociological Methods and Research, 50 (2), 567-596, doi: 10.1177/0049124118782550.
  • Becker, Devan G., Woolford, Douglas G. and Dean, C. B. (2021), Algorithmically deconstructing shot locations as a method for shot quality in hockey, Journal of Quantitative Analysis in Sports, 17 (2), 107-115, doi: 10.1515/jqas-2020-0012.
  • Becker, Woolford, D.G. and Dean, C.B. (2021), Visualization of Joint Spatio-Temporal Models via Feature Recognition with an Application to Wildland Fires, VISIGRAPP (3: IVAPP), 233-239.
  • Xi, Dexen D.Z., Dean, C.B., Renouf, E.M. (2021), Joint Modeling of Hospitalization and Mortality of Ontario Covid-19 Cases, in V.K. Murthy, J. Wu (eds.), Mathematics for Public Health: Fields Institute Communications, 85.
  • Bucyibaruta, G., Dean, C.B., Renouf, E.M. (2021), A logistically varying carrying capacity for Covid-19 deaths using data from Ontario, Canada, in V.K. Murthy, J. Wu (eds.), Mathematics for Public Health: Fields Institute Communications, 85.
  • Xi, Dexen D.Z., Dean, C.B., Taylor, S.W. (2021), Modeling the Duration and Size of Wildfires Using Joint Mixture Models, Environmetrics, 32 (6), doi: 10.1002/env.2685.
  • Juarez-Colunga, E. and Dean, C.B., (2020), Negative Binomial Regression, Wiley StatsRef: Statistics Reference Online, 1-8, doi: 10.1002/9781118445112.stat08246.
  • Xi, Dexen, D.Z., Dean, C.B. and Taylor, Stephen, W., (2019), Modeling the Duration and Size of Extended Attack Wildfires as Dependent Outcomes, Environmetrics, doi: 10.1002/env.2619.
  • Nadeem, K., Taylor, S.W., Woolford, Douglas G., Dean, C.B. (2019), Mesoscale Spatio-temporal Predictive Models of Daily Human and Lightning-caused Wildland Fire Occurrence in British Columbia, International Journal of Wildland Fire, doi: 10.1071/WF19058.
  • Becker, D.G., Braun, W.J., Dean, C.B. and Woolford, D.G. (2019), Visualizing Multivariate Time Series of Aerial Fire Fighting Data, Journal of Environmental Statistics, 9 (1), 1-15.
  • Xi, Dexen, D.Z., Dean, C.B. and Taylor, Stephen, W., (2019), Modeling the Duration and Size of Wildfires Using Joint Mixture Models, Environmetrics 32(3), doi: 10.1002/env.2685.
  • Albert-Green, A., Braun, W.J., Dean, C.B. and Miller, C. (2019), A Hierarchical Point Process with Application to Storm Cell Modelling, Canadian Journal of Statistics, 46 (1), 46-64, doi: 10.1002/cjs.11485.
  • Xi, D.D.Z., Taylor, S.W., Woolford, D.G. and Dean, C.B. (2019), Statistical Models of Key Components of Wildfire Risk, Annual Review of Statistics and Its Applications, 6, 197-222.
  • Luo, B, Edge, A.K.,Tolg, C., Turley, E.A., Dean, C.B., Hill, K.A., and Kulperger, R.J., (2018), Spatial Statistical Tools for Genome-Wide Mutation Cluster Detection under a Microarray Probe Sampling System, PloS one, 13(9), doi: 10.1371/journal.pone.0204156.
  • Juarez-Colunga, E., Silva, G.L., and Dean, C.B. (2017), Joint Modeling of Zero-inflated Panel Count and Severity Data, Biometrics, doi: 10.1111/biom.12691.
  • Wolters, M.A., and Dean, C.B. (2017) Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier, Statistics in Biosciences, doi: 10.1007/s12561-016- 9185-5.
  • Dean, C.B., Bull, S.B., Nadeem, K. and Wolters, M.A. (2017), Big Data in Biosciences, Wiley StatsRef: Statistics Reference Online, doi: 10.1002/9781118445112.stat07989.
  • Renouf, E., Dean, C.B., Bellhouse, D.R., and McAlister, V.C., (2016), Joint Survival Analysis of Time to Drug Change and a Terminal Event with Application to Drug Failure Analysis using Transplant Registry Data, International Journal of Statistics in Medical Research, 5(3), 189-197.
  • Hosseini, R., Newlands, N.K., Dean, C.B., and Takemura, A. (2015), Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data, Remote Sensing, 7(3), 2752-2780.
  • Wolters, M.A., and Dean, C.B. (2015), Issues in the Identification of Smoke in Hyper- spectral Satellite Imagery: a Machine Learning Approach, Current Air Quality Issues, Nejadkoorki, F. (ed.), In-Tech, doi: 10.5772/60214.
  • Woolford, D.G., Dean, C.B., Martell, D.L., Cao, J. and Wotton, B.M. (2014), Lightning- caused Forest Fire Risk in Northwestern Ontario, Canada is Increasing and Associated with Anomalies in Fire-Weather, Environmetrics, 25(6), 406-416, doi: 10.1002/env.2278
  • Feng, C.X., and Dean, C.B. (2014), Spatial Pattern Analysis of Multivariate Disease Data. Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases, Wiley, Newark, NJ, 283-299, Chen, Moulin and Wu (eds).
  • Juarez-Colunga, E. and Dean, C.B. (2014), Analysis of Over- and Under-Dispersed Data. Methods and Applications of Statistics in Clinical Trials: Planning, Analysis, and Inferential Methods, Volume 2, Wiley, Newark,NJ, 1-9, doi: 10.1002/9781118596333.
  • Juarez-Colunga, E., Dean, C.B., and Balshaw, R. (2014), Efficient Designs for Longi- tudinal Recurrent Event Studies with Missing Data, Biostatistics, 15(2), 234-50, doi: 10.1093/biostatistics/kxt054.
  • Mitnitski, A., Fallah, N., Dean, C.B., and Rockwood, K. (2014), A Multi-state Model for the Analysis of Changes in Cognitive Scores over a Fixed Time Interval, Statistical Methods in Medical Research, 23(3), 244-256, doi: 10.1177/0962280211406470.
  • Campbell, H.C., and Dean, C.B. (2014), The Consequences of Proportional Hazards Based Model Selection, Statistics in Medicine, 33(6), 1042-1056. doi: 10.1002/sim6021.

  • Taylor, S.W., Woolford, D.G., Dean, C.B., and Martell, D.L. (2013), Wildfire Prediction to Inform Fire Management: Statistical Science Challenges, Statistical Science, 28(4), 586-615, doi: 10.1214/13-STS451.

  • Feng, C.X., Dean, C.B. and Reich, R. (2013), Impact of Misspecifying Spatial Exposures in a Generalized Additive Modeling Framework: with Application to the Study of the Dynamics of Comandra Blister rust in British Columbia, Environmetrics, 24(2), 63-80, doi: 10.1002/env.2197.

  • Woolford, D.G., Cao, J., Dean, C.B., and Martell, D.L. (2010), Characterizing Temporal Changes in Forest Fire Ignitions: Looking for Climate Change Signals in a Region of the Canadian Boreal Forest, Environmetrics, 21, 789-800.

  • Nielsen, J.D. and Dean, C.B. (2008), Clustered Mixed Non-homogeneous Poisson Process Spline Models for the Analysis of Recurrent Event Panel Data, Biometrics, 64, 751-761, doi:10.1111/j.1541-0420.2007.00940.x.

  • Nathoo, F. and Dean, C.B. (2008), Spatial Multi-State Transitional Models for Longitudinal Event Data, Biometrics, 64, 271-279, doi:10.1111/j.1541-0420.2007.00785.x.

  • Ainsworth, L.M. and Dean, C.B. (2008), Detection of Local and Global Outliers in
    Mapping Studies, Environmetrics, 19, 21-37, doi:10.1002/env.851.

  • Nathoo, F. and Dean, C.B. (2007), A Mixed Mover-Stayer Model for Spatio-Temporal Two-State Processes, Biometrics, 63, 979-986; doi:10.1111/j.1541-0420.2007.00752.x.

  • Dean, C.B., Nathoo, F., and Nielsen, J.D. (2007), Spatial and Mixture Models for
    Recurrent Event Processes, Environmetrics, 18, 713-725.

  • Balshaw, R. and Dean, C.B. (2002), A Semi-Parametric Model for Recurrent Event
    Panel Data, Biometrics, 58, 324-331.

  • MacNab, Y. and Dean, C.B. (2001), Autoregressive Spatial Smoothing and Temporal
    Spline Smoothing for Mapping Rates, Biometrics, 57, 949-956.

  • Dean, C.B., Ugarte, M.D. and Militino, A.F. (2001), Detecting Interaction between
    Random Region and Fixed Age Effects in Disease Mapping, Biometrics, 57, 197-202.

  • Dean, C.B. and Balshaw, R. (1997), Efficiency lost by analyzing counts rather than event times in random effects Poisson processes, J. Amer. Statist. Assoc., 92, 1387-1398.

  • Dean, C.B. (1992), Testing for overdispersion in Poisson and binomial regression models, J. Amer. Statist. Assoc., 87, 451-457.

  • Dean, C. and Lawless, J.F. (1989), Tests for detecting overdispersion in Poisson regression models, J. Amer. Statist. Assoc., 84, 467-472.