Data Science and Machine Learning (DSML) have revolutionized the field of public health microbiology and genomics, enabling transformative advancements in practice and research. This Microbiology Rounds presentation will introduce innovative DSML approaches that leverage large-scale and heterogeneous datasets, including genomic sequences, epidemiological, clinical and environmental data, to gain deeper insights into the dynamics of infectious diseases. The presenter will show how DSML techniques can facilitate the identification of disease patterns, outbreak detection, antimicrobial resistance prediction, and the discovery of novel pathogen strains by using sophisticated data analysis and predictive modeling. Moreover, the fusion of data science with pathogen genomics drives real-time surveillance, aiding in the understanding of disease transmission dynamics and the identification of genetic markers associated with virulence and drug resistance. The presentation will also discuss considerations that must be given to data quality, suitability and the interpretability of complex models as the field progresses; which can enhance public health outcomes and inform evidence-based decision-making.
Intended audience: Microbiologists, Laboratory Scientists, Biologists, Public Health professionals interested in Data Science and Machine Learning. Researchers. Epidemiologists.
By the end of this session, participants will be able to:
- Describe the main principles of DSML
- Identify data challenges and opportunities to optimize the DSML pipeline
- Discuss applications of DSML to public health microbiology and genomics practice and research examples including WGS-based pathogen surveillance and antimicrobial resistance
Presenter(s): Dr. Venkata R. Duvvuri
Dr. Venkata R. Duvvuri, Ph.D, MPH, Scientist, Public Health Ontario and Assistant Professor at Laboratory Medicine and Pathobiology, University of Toronto. Dr. Duvvuri leads an applied genomic and machine learning program to translate pathogen surveillance for enhancing public health practice and response, and developing effective interventions. His research utilizes data science and machine learning and genomic epidemiology and phylodynamics approaches.
Disclaimer
The opinions expressed by speakers and moderators do not necessarily reflect the official policies or views of Public Health Ontario, nor does the mention of trade names, commercial practices, or organizations imply endorsement by Public Health Ontario.
Accreditation
Public Health Ontario Rounds are a self-approved group learning activity (Section 1) as defined by the Maintenance of Certification Program of the Royal College of Physicians and Surgeons of Canada (RCPSC). In order to receive written documentation for Continuing Medical Education (CME) credits, please check “Yes” beside the question “Do you require CME credits?” on the registration form.
College of Family Physicians of Canada (CFPC) Affiliate Members may count RCPSC credits toward their Mainpro+ credit requirements. All other CFPC members may claim up to 50 Certified credits per cycle for participation in RCPSC MOC Section 1 accredited activities.
PHO Rounds are also approved by the Council of Professional Experience for professional development hours (PDHs) for members of the Canadian Institute of Public Health Inspectors (CIPHI).
For more information or for a record of registration for other Continuing Education purposes, please contact capacitybuilding@oahpp.ca.
Accessibility
Public Health Ontario is committed to complying with the Accessibility for Ontarians with Disabilities Act (AODA). If you require accommodations to participate in this event, please contact 647-260-7100 or capacitybuilding@oahpp.ca.
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