October 2022 CIC Webinar Recap
Editor’s Note:
Guest Post: Rahul Singh
The COVID Information Commons (CIC) webinar took place on October 4th, 2022. In this forum, leading COVID-19 scientists presented their current research on the global pandemic.
Event moderators included Florence Hudson, Executive Director of the Northeast Big Data Innovation Hub at Columbia University and COVID Information Commons Principal Investigator (PI), Lauren Close, Operations & Communications Manager, and Emily Rothenberg, NSDC Program Coordinator.
The researchers presented a range of topics, each touching on broader themes related to the COVID-19 pandemic. Two presentations spoke about the pandemic’s impact on social and behavioral aspects of graduate students and native communities, and one talked about using Machine Learning to identify variations in viral infections. These research projects were funded by the National Science Foundation (NSF).
The session started with a presentation from Jennifer Cromley (University of Illinois at Urbana-Champaign) and her colleagues Karin Jensen (University of Michigan) and Joe Mirabelli (University of Illinois at Urbana-Champaign). Dr. Cromley discussed their research project, Understanding graduate engineering student well-being for prediction of retention.This grant is funded by the NSF Division of Engineering Education and Centers.
The initial literature review of their research showed that COVID-19 fear was resulting in stress related to finance, job loss, productivity, social isolation, and travel among graduate students. For this study, 55 students from 12 engineering departments were interviewed. The students were 53% female, 47% white, 36% Asian, and 36% first-generation university graduates at one U.S. university. An interview coding scheme around COVID-19 was developed with 29 specific codes representing various factors like housing, food, paid work, advisors, labs, courses, stress effects, and coping strategies throughout the pandemic.
The results from this research showed that COVID-19 had significantly affected the lives of these graduate students. Additionally, the impact of stress was more prominent in their personal lives than in their professional lives. Factors such as inability to travel, fear of getting sick, fear for family and friends exposed to COVID, inability to regularly exercise during the lockdown, disrupted long-term life plans, lack of access to a community, and visa restrictions were found to be higher-level stressors in comparison to factors like online classes and slowing of research in laboratories. Finally, it was also found that continually changing government, university, and county policies and regulations were affecting mental health significantly.
A video of Jennifer, Karin and Joe’s presentation can be found on the CIC website.
Next, we heard a presentation from Ilya Goldberg (ViQi Inc.) on Machine Learning for Early Detection of COVID-19 Plaques in Cells. This project was funded by the NSF TIP Directorate for Technology, Innovation and Partnerships.
Ilya’s organization uses cloud-based artificial intelligence imaging and analysis to perform microscopy tasks. The project focuses on the detection of virus infection in brightfield microscopy images and tests for the possibility of checking variations in the virus. The study was based on viruses producing membraned structures nearly 400 nm in size that show the lifestyle of these viruses on different host bodies. The AI algorithm understands these variations generated by the various host bodies and detects the type of virus causing these infections.
This research project has built a solution for detecting first-round infections and reducing incubation time with fewer processing steps. It provides results showing viral infectivity within one day and identifies infected cells with high accuracy. This solution is highly scalable and automated, and can be operated without any external setup or complex laboratory equipment since the program is hosted on the cloud. Given two images, one of an infected cell and another of a healthy cell, the AI can detect differences between the two and identify the infected cell with its degree of infection. This research has so far successfully been able to detect HIV, influenza, polio, and some other viral infections. Ilya’s team is working with coronaviruses in the hopes that this technology will support vaccine development and discovery.
A video of Ilya’s video presentation can be found on the CIC website.
The webinar ended with a presentation from Taylor Van Doren (Sitka Sound Science Center) on her research: Survey Study of COVID-19 Responses in Southeast Alaska. This project was funded by the NSF Office of Polar Programs (OPP).
Taylor’s research considers the impact of the COVID-19 pandemic on indigenous communities in Alaska and compares their experiences to historical pandemic responses in those communities. From literature surveys, she deduced that indigenous communities worldwide have faced disproportionately severe consequences as a result of health pandemics.
This study was conducted with 23 in-depth interviews with Alaska natives across 4 communities. A qualitative thematic analysis of these interviews was done to determine the main focuses of the research, including risk perception, socio-economic factors, community impacts, and adaptation to public health guidelines. The results of the study showed that many Alaskan natives (70%) expressed heightened concern for the health and well-being of vulnerable populations during the pandemic. Of the group surveyed, 30% had no historical knowledge of health epidemics, but the remaining sample shared community knowledge from the 1918 influenza epidemic.
Van Doren’s research showed that the community-centric attitudes shared by Alaska natives led to the easy and swift adoption of vaccines and health safety guidelines within the population. The study also acknowledged that rapid environmental changes have affected Native Alaskan populations in unique ways.
The study concluded that Native Alaskan populations persevered in the COVID-19 pandemic by relying on cultural history, identity, and community. Community support in times of adversity was critical to Native Alaskans’ health and well-being. Environmental adversity was found to be an added threat to their crisis triage mechanism.
A video of Taylor’s session can be found on the CIC website.
Following the presentations, Florence Hudson, Lauren Close, and Emily Rothenberg hosted a Q&A session where the audience engaged in a rich discussion with the researchers.These talks offered great insights into the impact of COVID-19 on health, education, and communities.
A recording of this event is available on the Northeast Big Data Innovation Hub’s YouTube Channel and the COVID Information Commons website. The COVID Information Commons is an NSF-funded project brought to you by the Big Data Innovation Hubs, led by the Northeast Big Data Innovation Hub at Columbia University.
We look forward to welcoming you to our next webinar. Stay tuned!