Publications

Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Quebec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David Buckeridge
Marc Brisson
Beate Sander
Adrienne K. Chan
Aman Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu-Giroux
Background: Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in inte… (see more)nsive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. Methods: We used population-based provincial hospitalization data from Ontario and Qu&eacutebec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Results: Using all hospitalizations from Ontario (N=26,541) and Qu&eacutebec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Qu&eacutebec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Qu&eacutebec by 3-6 days. Conclusion: In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
2021-12-08
medRxiv (preprint)
doi.org
Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization
Albert Manuel Orozco Camacho
Reihaneh Rabbany
In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important event… (see more)s happening around the world. In this paper, we, firstly, present a curated set of datasets whose origin stem from the Twitter’s Information Operations efforts. More notably, these accounts, which have been already suspended, provide a notion of how state-backed human trolls operate.Secondly, we present detailed analyses of how these behaviours vary over time,and motivate its use and abstraction in the context of deep representation learning:for instance, to learn and, potentially track, troll behaviour. We present baselinesf or such tasks and highlight the differences there may exist within the literature.Finally, we utilize the representations learned for behaviour prediction to classify trolls from"real"users, using a sample of non-suspended active accounts.
2021-12-07
LatinX in AI at Neural Information Processing Systems Conference 2021 (published)
doi.org
openreview.net
Flexible Option Learning
Martin Klissarov
Doina Precup
2021-12-06
ArXiv (preprint)
arxiv.org
arxiv.org
Flexible Option Learning
Martin Klissarov
Doina Precup
2021-12-06
ArXiv (preprint)
arxiv.org
arxiv.org
Genomic epidemiology and associated clinical outcomes of a SARS-CoV-2 outbreak in a general adult hospital in Quebec
Bastien Paré
Marieke Rozendaal
Sacha Morin
Raphael Poujol
Fatima Mostefai
Shawn M. Simpson
Jean-Christophe Grenier
Léa Kaufmann
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Julie Hussin
Guy Wolf
Ivan Pavlov
Martin Smith
2021-12-02
PLoS ONE (published)
doi.org
Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes
Bastien Paré
Marieke Rozendaal
Sacha Morin
Léa Kaufmann
Shawn M. Simpson
Raphael Poujol
Fatima Mostefai
Jean-Christophe Grenier
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Julie Hussin
Guy Wolf
Ivan Pavlov
Martin Smith
2021-12-02
PLoS ONE (published)
doi.org
Adapting to the COVID‐19 pandemic in cohort studies: Validation of online assessments of cognition and neuropsychiatric symptoms in an aging population
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
The occurrence of the COVID‐19 pandemic has had a significant impact on cohort studies, particularly those whose subjects are at higher ri… (see more)sk of developing complications from the virus. As such, assessment methods must be adapted to minimize COVID‐19 exposure risk. The TRIAD (Translational Biomarkers of Aging and Dementia) cohort assessed N=292 individuals during initial COVID‐19 lockdown measures by telephone interview to rate cognition, neuropsychiatric symptoms, and impact of the pandemic. To increase speed and efficiency of data collection, we aim to follow these individuals by means of online survey. Here, we present a validation of our online assessment tools by comparing data obtained through both methods (phone interview and online survey) in the same subjects.
2021-12-01
Alzheimer's & Dementia (published)
doi.org
Cognitive health mediates the effect of hippocampal volume on COVID‐19‒related knowledge or anxiety change during the COVID‐19 pandemic
Min Su Kang
Julie Ottoy
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
2021-12-01
Alzheimer's & Dementia (published)
doi.org
Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform
Dmitriy Rivkin
David Meger
Di Wu
X. T. Chen
Xue (Steve) Liu
Gregory Dudek
We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization… (see more) algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.
2021-12-01
Global Communications Conference (published)
doi.org
Online Partisan Polarization of COVID-19
Zachary Yang
Anne Imouza
Kellin Pelrine
Sacha Lévy
Jiewen Liu
Gabrielle Desrosiers-Brisebois
Jean-François Godbout
André Blais
Reihaneh Rabbany
In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several im… (see more)portant issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians’ management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter.
2021-12-01
2021 International Conference on Data Mining Workshops (ICDMW) (published)
doi.org
Tau‐load in the lingual gyrus impacts anxiety levels during the COVID‐19 pandemic in participants of longitudinal observational studies in aging
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Yi‐Ting Wang
Jenna Stevenson
Cécile Tissot
Guillaume Elgbeili
Jaime Fernandez Arias
Joseph Therriault
Andréa Lessa Benedet
Mira Chamoun
Tharick A. Pascoal
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
2021-12-01
Alzheimer's & Dementia (published)
doi.org
Tau‐PET is associated with knowledge of COVID‐19, COVID‐19‐related distress, and change in sleep quality during the pandemic
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Guillaume Elgbeili
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
While the global COVID‐19 pandemic has hindered many human research operations, it has allowed for the investigation of novel scientific q… (see more)uestions. Particularly, the effects of the pandemic and its resulting social isolation on elderly individuals and their association with Alzheimer’s disease biomarkers remains a broad and open question. Here, we sought to investigate whether knowledge of COVID‐19, pandemic‐related distress, and changes in sleep quality were associated with in vivo tau deposition in an AD‐enriched cohort.
2021-12-01
Alzheimer's & Dementia (published)
doi.org