Practicum Student – AI-driven respiratory virus forecasting

Full Description

Description of the placement:

We are looking for a student interested in infectious disease epidemiology and machine learning methods who is seeking an opportunity to develop their epidemiologic and analytic skills.

This project will focus on advancing machine learning methods for short-term (“nowcasting”) surveillance of SARS-CoV-2, influenza, and RSV activity in Ontario.

The student will contribute to the development and evaluation of a range of algorithms—e.g., Random Forest, Extreme Gradient Boosting (XGBoost)—using time series cross-validation to assess predictive performance.

The project will also explore ensemble forecasting strategies that intelligently combine outputs from multiple models to balance sensitivity and stability.

By improving the precision and reliability of respiratory virus forecasts, this initiative will enhance public health situational awareness and support timely, evidence-informed decisions in outbreak response, resource allocation, and health system planning.

Working with Scientists and staff from the Communicable Disease Control, Communicable Diseases, and Data Science and Analytics teams, the student will have the opportunity to develop analytic plans and undertake statistical analyses.

The student may also contribute to the preparation of a report or manuscript.

This practicum is an ideal fit for a student with strong statistical analysis skills who wishes to gain experience in infectious disease epidemiology.

The student will work as part of a multidisciplinary research team to conduct the work and will have the opportunity to learn more about the datasets available at PHO and how they can be used to contribute to public health research.

Suggested placement start date: May to August 2026

Please note:

•This position is only open to students who are currently enrolled in an academic program where the completion of a practicum placement/internship is required.

•This position is only open to students currently residing in Ontario and attending a Canadian academic institution. Preference will be given to students enrolled in Ontario-based academic institutions.

•This is a paid placement.

Educational objectives

:

• Develop and apply analytical skills (e.g., machine learning, forecasting, ensemble models).

• Practice communication skills (i.e., abstract and manuscript writing, team presentations).

• Gain experience in public health and working with laboratory testing data.

Proposed deliverables at the end of the placement:

• An abstract for submission to a conference.

• A manuscript for submission to a peer-reviewed journal or report delivered to the team.

Required education and experience:

• Currently enrolled in a Master’s degree in biostatistics, epidemiology, computer science, data science, or similar where the completion of a practicum/internship placement is required.

• Good knowledge of R for data manipulation and analysis preferred, but experience with another analytic software will be considered.

• At least a basic understanding of machine learning and forecasting methods.

• Familiarity with infectious disease epidemiology, would be considered an asset.