The Surveillance Lab

The Surveillance lab within the Clinical and Health Informatics Research Group at McGill University brings together a vibrant multidisciplinary team of over 20 investigators, public health practitioners, clinicians, research staff, students and software developers, all dedicated to conducting research and development of computational methods and software that has immediate impact on improving population health through the science and practice of biosurveillance. The Surveillance Lab is funded by several sources including the Canadian Foundation for Innovation, the Canadian Institutes of Health Research, a Canada Research Chair, the Bill and Melinda Gates Foundation, the National Sciences and Engineering Research Council, the Centers for Disease Control and Prevention and many other sources. On many projects, we work closely with public health practitioners in Quebec and from around the world. The computerized solutions we have developed are used by public health agencies in Quebec, Canada, and internationally.

Lab Lead


David L. Buckeridge

Professor

David Buckeridge is a Professor of Epidemiology and Biostatistics at McGill University in Montreal where he holds a Canada Research Chair in Health Informatics and Data Science. He is also a Medical Consultant to the Institut national d’excellence en santé et en services social. Dr Buckeridge has consulted on surveillance to organizations such as the Public Health Agency of Canada, the US Institute of Medicine, the US and Chinese Centers for Disease Control, the European Centers for Disease Control, and the World Health Organization. He holds a M.D. from Queen’s University, a M.Sc. in Epidemiology from the University of Toronto, a Ph.D. in Biomedical informatics from Stanford University and is a Fellow of the Royal College of Physicians and Surgeons of Canada with specialty training in Public Health and Preventive Medicine.


    Yu Luo Post Doc

      Xuefei Shi Developer / Développeuse

        Guido Powell Research Assistant / Assistant de recherche

          Alexis Hamel Developer / Développeur

            Mengru Yuan Research Assistant / Assistante de recherche

              Anya Okhmatovskaia Research Associate / Associé de recherche

                Maxime Lavigne PhD Student / Doctorant

                  Aman Verma Research Associate / Associé de recherche

                    Hiroshi Mamiya PhD Student / Doctorant

                      Erin Yiran Liu PhD Student / Doctorante

                        Daniela Anker Research Trainee / Stagiaire de recherche

                        Publication majeure

                        Below you will find some of the main publications of the surveillance laboratory. For a more complete list, please visit Dr. Buckeridge’s Google Scholar page.

                        Shaban‐Nejad A, Lavigne M, Okhmatovskaia A, Buckeridge DL. PopHR: a knowledge‐based platform to support integration, analysis, and visualization of population health data. Annals of the New York Academy of Sciences. 2017 Jan;1387(1):44-53.

                        Izadi M, Shaban-Nejad A, Okhmatovskaia A, Mondor L, Buckeridge DL. Population health record: an informatics infrastructure for management, integration, and analysis of large scale population health data. InWorkshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence 2013 Jun 29.

                        Buckeridge DL, Izadi M, Shaban-Nejad A, Mondor L, Jauvin C, Dube L, Jang Y, Tamblyn R. An infrastructure for real-time population health assessment and monitoring. IBM Journal of Research and Development. 2012 Aug 7;56(5):2-1.

                        Current Projects

                        PopHR

                        The Population Health Record (PopHR) is an informatics platform that uses existing epidemiological and public health knowledge to integrate multiple clinical and administrative data sources to provide a coherent view of the health of populations. Users of the PopHR can develop detailed portraits of the health status and healthcare utilization patterns for a population, monitor various health indicators to detect temporal and spatial variations in disease activity, and evaluate the effectiveness of interventions on population health. The platform provides representative information in near-real time with high geographical resolution, thereby assisting public health professionals, clinicians and the public in diagnostic and therapeutic decision-making. At the same time, the PopHR provides a platform for advancing research in public health informatics and disease surveillance.

                        To view the Canada-wide version of the software please click here. Please note that it is optimized for use in the Chrome Browser. Additional information on the POPHR can also be found in our Gitbook.

                        Funded By: Public Health Agency of Canada

                        *The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.

                        E-Health Interventions in Public Health

                        We all use information to make decisions. In our daily life, we have seen remarkable gains in the ease with which we can access information. Unfortunately, similar advances in information access have not occurred in public health practice, where it is often difficult to access necessary information. There is a huge potential benefit to society if we can develop and evaluate software to help people make better decisions about preventing chronic diseases, detecting infectious disease outbreaks, and planning the delivery of public health services. This research program will develop and evaluate innovative strategies that use modern computing to improve decisions about important public health problems.

                        Funded By: Canadian Institutes for Health Research

                        In Collaboration with: Sante Montreal, INSPQ, NCCPH, Stanford University, McGill University

                        Surveillance of Vaccination Opinions and Beliefs in Online Media

                        The goal of this project is to develop methods for public health surveillance of opinions and beliefs about vaccination expressed in online media. In particular, we intend to automate the detection and classification of comments about the safety and effectiveness of vaccines. To develop these methods we are using a large sample of media reports from HealthMap’s Vaccine Intelligence Surveillance System. The automated methods will be evaluated against manual classification of the same reports. As we develop and evaluate our methods, we are also performing descriptive analyses to understand how the frequency of media reports containing comments about vaccines changes over time and space. This work will provide evidence to guide the use of online media surveillance and monitor vaccine opinions and beliefs.

                        Funded By: CIRN

                        In Collaboration with: Concordia University and Harvard University

                        Past Projects

                        Opioid Overdose

                        The use of illegal drugs imposes a devastating cost on Canadians, both in terms of lives lost and dollars spent. The rate of prescribing opioids, in particular, has increased close to one hundred-fold since 2000 and more Canadians are now dying of accidental overdoses from POs than from street drugs such as heroin and cocaine. Prevention efforts are needed urgently, but detecting individuals likely to experience a PO overdose is currently difficult. The goal of this project is to identify personal characteristics and patterns of healthcare use associated with unintentional death from PO overdose. We will use health data from multiple sources, including the Coroner’s Office, hospital records and drug prescribing databases, to identify characteristics of PO abusers who are likely to die from an accidental overdose. This information should allow public health policy-makers to optimize treatment services and harm reduction efforts.

                        Funded By: Canadian Institutes for Health Research

                        Scalable Data Integration for Disease Surveillance (SDIDS)

                        Data for global disease surveillance are fragmented across diseases, countries, funders, and a wide range of clinical settings. This fragmentation of data makes it challenging to assess population health, target disease control activities, and evaluate the effect of interventions. We are developing the SDIDS software platform to integrate surveillance data and make them available to support global health decision making. A proof-of-concept version of the system has been to integrate malaria surveillance data for Uganda. SDIDS makes extensive use of ontologies, including an ontology of data sources and an ontology of global health. Raw data sources are mapped to these ontologies and then automatically translated into a common format, where the integrated data can then be accessed by software to calculate and visualize a variety of indicators. We are now in the process of scaling-up SDIDS to include data for the main causes of under-five mortality in Africa.

                        Funded by: Bill and Melinda Gates Foundation

                        In Collaboration with the Uganda Malaria Surveillance Project and the National Malaria Control Programme, University of Washington, Stanford University, Harvard University.

                        GI Project

                        Reviews of the public health response to historical outbreaks of GI illness call consistently for improvements to the public health surveillance. There is not sufficient evidence, however, to determine the effectiveness of specific changes to the existing surveillance infrastructure. The goal of the project is to evaluate empirically how enhancements to public health surveillance systems will impact the effectiveness of these systems in detecting waterborne enteric disease outbreaks.

                        Former Members

                        Luc de Montigny

                        Kate Zinszer

                        Arash Shaban-Nejad

                        Deepa Jahagirdar

                        Jean-Paul Soucy

                        Marc-Andre Blanchette

                        Yun-Hsuan Wu (Wendy)

                        Kathryn Morrison

                        Kody Crowell