Daniel McDonald (University of British Columbia) is the recipient of the 2026 Lise Manchester Award. This award is given every other year by the Statistical Society of Canada to commemorate the late Dr Lise Manchester’s abiding interest in using statistical methods to study matters of relevance to society. The award recognizes excellence in statistical research that helps guide public policy in Canada.
Daniel J. McDonald obtained his undergraduate degrees in 2006 from Indiana University: a Bachelor of Arts in Economics (Summa cum laude) and a Bachelor of Science in Music -- Cello Performance -- and Mathematics (Magna cum laude). He completed his MSc (2008) and PhD (2012) in Statistics at Carnegie Mellon University. He returned to Indiana University to begin his academic career, and then in 2020 joined UBC, where he is now Professor of Statistics.
In 2012, faculty in Statistics and Machine Learning at Carnegie Mellon founded the Delphi Research group to develop the theory and practice of epidemic detection, tracking and forecasting, with an emphasis on respiratory viruses. With the onset of the COVID-19 pandemic in early 2020, many scientists across the world pivoted their work to aid public health agencies, and Daniel, among others, joined the Delphi Group to speed up COVID-related model and forecast development and validation, as well as signal extraction, data curation and pipeline automation. Over the next five years, Daniel took on leading roles in forecasting operations, software development and overall scientific direction. He was the main developer of the ‘epipredict’ R package for building modular forecasting models and remains its maintainer. He also helped to grow Delphi’s Epidata platform which delivers thousands of real-time, version aware, disease-related signals at the finest possible geographic, demographic and temporal granularity.
The Lise Manchester Award is for a research achievement that will “help guide public policy in Canada”. In this case, Daniel’s work with the Delphi Group provided a steppingstone to related epidemic forecasting efforts in Canada, first with the BC COVID-19 Modelling Group, and subsequently with teams from British Columbia’s CDC. The BC COVID-19 Modelling Group, led by Sally Otto (UBC, Tier 1 CRC in Theoretical and Experimental Evolution), Dan Coombs (Department of Mathematics, Institute of Applied Mathematics), and Caroline Colijn (Tier 1 CRC in Mathematics for Infection, Evolution and Public Health), produced a number of public reports for dissemination to the media and BCCDC, to which Daniel contributed statistical analysis and interpretation. Several of his UBC graduate students worked on Delphi projects that were of benefit to Canada: Rachel Lobay on reconstructing latent infections from seroprevalence and wastewater data; Jiaping Liu on real-time estimation of the effective reproduction number using deconvolution; Sarah Masri on connecting epidemic compartmental models and Hawkes processes; and Christine Chuong on curating PHAC’s RVDSS data and setting up a respiratory forecasting hub for Canada. Christine continues to work with BCCDC developing facility-level forecasters across syndromes in preparation for this summer’s FIFA World Cup.
Daniel’s research program continues to address problems in estimating the scope and progress of an epidemic, building on earlier work that evaluated the use of auxiliary signals for forecasting (PNAS, 2021) and methods for smoothly estimating time-varying epidemiological parameters (JSS, 2024). For instance, the PhD thesis of Daniel's student Rachel Lobay develops and applies methodology for retrospective estimation of latent COVID infections, providing insight into the disease’s dynamics. Her work, published in 2025 and 2026 in Epidemics, provides methodology to handle the under-reporting of COVID cases, and leverages information from seroprevalence surveillance data and wastewater measurements.
Daniel’s current work builds on these efforts, connecting time series models, and nonparametric regression, especially in the context of estimating latent parameters from epidemiological data.
“To Daniel McDonald, for his contributions to the evidence-based prediction of the spread of COVID-19. Through his work with the British Columbia COVID-19 Modelling Group and the US based Delphi Research Group, Daniel provided public health officials with reliable forecasts. Through his leadership in Delphi, he created informative data bases and made them easily accessible to other researchers, and he developed state of the art open-source code. Through a series of publications, Daniel has made available his methodological advancements in forecasting and nowcasting of the spread of disease using complex data sources.”