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Informing response to COVID19 using mathematical and machine learning modeling and analytics

Organiser

Sevgui Erman

Participants

Louis Borgeat (Chair)

Ashkan Ebadi (Presenter/Speaker)

COVID-19 screening tools based on medical images using few-shot learning strategies

Dr Miroslava Čuperlović-Culf (Presenter/Speaker)

Machine learning modeling of the effect of COVID19 on metabolic network.

James Ooi (Presenter/Speaker)

COVID-19 in-host modelling: applying mechanistic models to understand the vaccine-induced immune response.

Steffany Bennett (Presenter/Speaker)

Impact of COVID-19 on sex-specific risk of altered cognitive performance and dementia in context of biological equity, diversity and inclusion using big data/machine learning approaches.

Conference

64th ISI World Statistics Congress

Category: International Statistical Institute

Abstract

Global efforts have gone into building accurate and predictive models of different aspects of COVID-19 from the limited but ongoing flow of data available during the pandemic, in the hope of developing better diagnostic, prognostic, treatment, and public health tools, but also to better understand equity, diversity, inclusion impacts of the pandemic.

This session will present the outcome of multiple collaborations between the Digital Technology research center at the National research council of Canada, and it’s network of academic partners across the country. Presentations will cover a wide range of successful modeling efforts, from in-host models for current vaccines that help to understand the vaccine-induced immune response, to predictive models of COVID resilience and response built from molecular data, impact of COVID on equity, diversity and inclusion indicators, and new diagnostic COVID-19 screening tools based on medical images, using few-shot learning strategies.