Research

Peer-reviewed journal articles

Statistical Papers

JJ Slater, PE Brown, JS Rosenthal, J Mateu (2024+). Leveraging cellphone-derived mobility networks to assess COVID-19 travel risk. To Appear in Annals of Applied Statistics. (pdf)

JJ Slater, A. Bansal, H. Campbell, JS Rosenthal, P. Gustafson, PE Brown (2024). A Bayesian approach to estimating COVID-19 incidence and infection fatality rates. Biostatistics, 25(2), 354-384. (paper) 

JJ Slater, PE Brown, JS Rosenthal, J Mateu (2022). Capturing spatial dependence of COVID-19 case counts with cellphone mobility data. Spatial Statistics 49: 100540.(paper)

JJ Slater, PE Brown, JS Rosenthal (2021). Forecasting subnational COVID-19 mortality using a day-of-the-week adjusted Bayesian hierarchical model. Stat, 10(1), e328 (paper) *Top 10 Wiley downloaded article.

A Béliveau, DJ Boyne, JJ Slater, D Brenner, P Arora (2019). BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses. BMC Medical Research Methodology, 19(1), 196. paper, R-package 

Applied papers (select). See my Google Scholar page for a complete list.

M Silverman, JJ Slater, R Jandoc, S Koivu, AX Garg, MA Weir (2020). Hydromorphone and the risk of infective endocarditis among people who inject drugs: a population-based, retrospective cohort study. The Lancet Infectious Diseases, 20(4), 487-497.

M Ordon, J Dirk, JJ Slater, J Kroft, S Dixon, B Welk (2020). Incidence, Treatment, and Implications of Kidney Stones During Pregnancy: A Matched Population-Based Cohort Study. Journal of Endourology, 34(2), 215-221.

MA Weir, JJ Slater, R Jandoc, S Koivu, AX Garg, M Silverman (2019). The risk of infective endocarditis among people who inject drugs: a retrospective, population-based time series analysis. CMAJ, 191(4), E93-E99.

Gupta, A., JJ Slater, Boyne, D.,  and others (2019). Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method. Medical Decision Making, 39(8), 1032-1044.

P Arora, D Boyne, JJ Slater, A Gupta, DR. Brenner, and M Druzdzel (2019). "Bayesian networks for risk prediction using real-world data: a tool for precision medicine." Value in Health 22, no. 4: 439-445. 

Teaching Pedagogy

S Kang, JJ Slater (2022). Pop Quizzical: Does Authoring questions for peers improve learning in Introductory Statistics?

Research Talks, Posters, and Workshops

Upcoming talks

A practical look at network autoregressions for underreported infectious disease data (slides)


Leveraging cellphone-derived mobility networks in spatiotemporal infectious disease models

Quantifying the risk associated with travelling during a pandemic using cellphone-derived mobility networks (based on joint work with PE Brown, JS Rosenthal, and J Mateu)

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates (based on joint work with A Bansal, H Campbell, JS Rosenthal, P Gustafson, PE Brown)


Modelling the COVID-19 pandemic using population mobility data (based on joint work with PE Brown, JS Rosenthal, J Mateu)

BUGSnet: A New Comprehensive R Package for Network Meta-Analysis (based on joint work with A Beliveau, DJ Boyne, DR Brenner, P Arora)

My contribution was a ~30 minute segment where I introduced basic network meta-analysis concepts followed by a step-by-step tutorial on how to apply these concepts in BUGSnet.