To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca

Presentation 1

Time: 11:00am – 11:30am

Speaker: Sarah Masri, UBC Statistics MSc student

Title: Compartmental models and Hawkes processes: equivalence and computational advantages in epidemiological modelling

Abstract: Epidemiological modelling is crucial for understanding and responding to the spread of infectious diseases, helping public health officials assess the impact of interventions and inform policy decisions. Some prominent models within this field, such as the SIR and SEIR compartmental models, rely on unobserved measurements and can be computationally intensive. This thesis investigates the equivalence between the stochastic SIR and SEIR compartmental models and the Hawkes process, a self-exciting point process, in the epidemiological setting. The research demonstrates that, under specified conditions, the SIR and SEIR models can be interpreted as special cases of the finite population Hawkes process, offering a unified framework for disease modelling that does not rely on latent measurements. This thesis contributes to the growing body of literature on stochastic epidemic models by providing an alternative approach to complement compartmental models, highlighting how inference under the Hawkes process, when fitting the process to data, is consistent with the parameters associated with the SIR and SEIR models. The findings suggest that the Hawkes process can approximate some compartmental models, offering a promising tool for epidemiological modelling.

Presentation 2

Time: 11:30am – 12:00pm

Speaker: Christine Chuong, UBC Statistics MSc student

Title: Forecasting Influenza, COVID-19 and Respiratory Syncytial Virus Detections in Canada

Abstract: Respiratory illnesses such as influenza, respiratory syncytial virus, and COVID-19 result in many hospitalizations and deaths per year in Canada. Anticipating the behaviour of viruses can be challenging as behaviour can differ between seasons, so accurate forecasts of future behaviour can help reduce uncertainty. Modelling hubs can be a useful tool in collecting and evaluating multiple forecasts in one place. Hubs run forecasting challenges that invite teams to make weekly probabilistic short-term forecasts for illnesses of interest, but no national challenge predicting respiratory viruses exists in Canada. Using data on respiratory virus detections taken from historic reports and an interactive dashboard maintained by the Public Health Agency of Canada, we established a forecasting hub for the 2024-2025 respiratory illness season. We submitted forecasts for a climatological model using historical data and an ensemble of this climatological model and an autoregressive with exogenous covariate (ARX) model with the predicted climatological medians. Forecasts were evaluated using the absolute error of the predicted median, weighted interval score (WIS) and empirical coverage of prediction intervals.