Date/Time
Date(s) - 19/09/2024
2:30 pm - 3:30 pm

Location: HH-410

Title: Drivers of Covid-19 Variant Wave Dynamics: A Statistical Approach Leveraging Global Data

Speaker: Shabnam Molan

Abstract: Throughout the pandemic, countries and scientists rushed to predict the impact of sequentially emerging variants by, in part, trying to predict the size of the wave likely to hit their local community. Our study aims to predict the amplitude of forthcoming COVID-19 waves using statistical modelling and a unique global dataset. Utilizing publicly available datasets, we compiled relevant features from demographics, geographic distribution, mobility patterns, epidemiological data, and immunity levels, which influence the spread and severity of COVID-19 waves. Moreover, through collaboration with the Public Health Agency of Canada, we developed viral genomic evaluation metrics, which allow for a nuanced understanding of the dynamic landscape of COVID-19 variants. To train our model, we used the amplitude of consecutive variant waves, specifically Delta, BA.1, and BA.2 waves. We trained a random forest model with a leave-one-out cross-validation strategy that respects the time series nature of the data from each country. Our model demonstrates robust performance in several scenarios, accurately predicting the relative size of forthcoming waves. The results underscore the critical role of ongoing epidemiological and genomic surveillance in enhancing our model’s predictive capabilities. By integrating real-time data, we are able to refine our forecasts and provide insights crucial for public health planning and response strategies.

Shabnam Molan is a PhD student at Simon Fraser University, supervised by Jessica Stockdale and Caroline Colijn.