Spring 2023 CIC Webinar Recap
Guest Post: Varalika Mahajan
The twenty-fifth in the series of COVID Information Commons (CIC) webinars which began in 2020 took place on April 24th, 2023. 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, National Student Data Corps (NSDC) Program Coordinator.
The researchers presented a range of topics, each touching on broader themes related to the COVID-19 pandemic. A total of five presentations were demonstrated and discussed as described below.
The session started with a presentation from Ellen Foxman from Yale University. Ellen discussed her research project, Host response-based screening for unexpected or emerging respiratory viruses. This research was funded by the NIH National Institute of Allergy and Infectious Diseases (NIAID).
Ellen and her team addressed the challenge of identifying undiagnosed viruses in patients who present with non-specific symptoms such as fever, cough, and shortness of breath. To screen for undiagnosed viruses, the researchers utilized the body's antiviral response by identifying elevated levels of the protein CXCL10 in the nose, which correlates with a significant viral load. By focusing on samples with elevated CXCL10 levels, they were able to identify samples that were most likely to contain undiagnosed viruses. They tested 359 samples and found that only 60 of them had the antiviral response activated in the nose. Subsequently, they conducted metagenomic sequencing on the top 10 samples by CXCL10 level, and discovered tens of thousands of reads from influenza C virus in one sample. This approach can save time and cost while efficiently identifying new viruses that are not commonly known.
A video of Ellen’s presentation can be found on the CIC website.
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Next, Ioannis Paschalidis from Boston University presented his research on Predictive Models of COVID-19 Severity and Patient Outcomes. This project was funded by NSF Predictive Intelligence for Pandemic Prevention Phase 1.
Mr. Ioannis and team developed models to predict patient outcomes by capturing the dynamic evolution of a patient's health progress while in the hospital using time buckets to drop information. These models utilized deep learning methodologies, including Long short-term memory (LSTM) and Transformer architecture, and were fed with six vital signals at different points in time. The models achieved up to 92 percent accuracy in predicting hospitalization outcomes, with factors associated with earlier health conditions and occupancy of the hospital being important predictors. The models also revealed the importance of social determinants of health in predicting hospitalization outcomes and identified a bias that resulted in false positive predictions for black individuals. The team also developed models to predict ICU admissions, which were found to be much more accurate than standard models. The vital score was identified as an essential factor in predicting ICU admissions, along with other variables such as Lactate dehydrogenase (LDH ) and C-reactive protein(CRP).
A video of Ioannis’s presentation can be found on the CIC website.
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Next, Jeffrey Townsend from Yale University presented his research on Infection by SARS-CoV-2 with alternate frequencies of mRNA vaccine boosting. This research was funded by the NSF Division of Environmental Biology (DEB).
Jeffrey Townsend along with a team of researchers conducted a study on the effectiveness of COVID-19 vaccines in preventing infections. They found that antibody levels are crucial in understanding vaccine effectiveness, with higher levels associated with longer immunity periods. The Pfizer Biontech vaccine demonstrated a 1.5 times higher antibody level than a natural infection. Additionally, the study compared the antibody levels of Pfizer Biontech, Moderna, J&J, and Oxford AstraZeneca vaccines to natural infection and found that mRNA vaccines provide better protection against breakthrough infections than natural infections and viral vector vaccines. The study also evaluated the efficacy of boosting, finding that antibody levels increased after a second dose, providing individuals with more extended protection against future infections. Furthermore, the team found that the reinfection rate went down from 32% to 11% for those who received a booster shot after 6 months compared to those who received a booster after 12 months.
A video of Jeffrey’s presentation can be found on the CIC website.
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Next, Chang-Yu Wu from University of Miami presented his research on Environmental Surveillance to Assess Aerosol Transmission Pathways of COVID-19 Enabled by On-The-Spot Sampling and Detection. This research was funded by the NSF Division of Chemistry, Bioengineering, Environmental, and Transport Systems (CBET).
Chang-Yu Wu and team have conducted a study on the importance of proper ventilation and air sampling in indoor spaces during the COVID-19 pandemic. The study found that the virus causing COVID-19 could be transported through aerosols to other rooms in the same building. The risk of infection was similar in the primary and secondary rooms, emphasizing the need for good ventilation when someone is sick in the residence. The study also demonstrated the importance of proper air sampling and the development of point-of-care detection devices to improve the efficiency of virus detection and risk assessment.
A video of Chang-Yu’s presentation can be found on the CIC website.
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The webinar ended with a presentation from Niema Moshiri from University of California, San Diego on his research on Massively scalable reference-guided Multiple Sequence Alignment of viral genomes. This project was funded by the NSF Division of Environmental Biology (DEB).
Niema Moshiri and team, have developed a new tool called Viral MSA that enables rapid multiple sequence alignment of ultra-large viral datasets. In a presentation, the tool was shown to be orders of magnitude faster than other existing tools, even when aligning just a thousand sequences. The accuracy of the alignments was also compared, and the results showed that the alignments computed by Viral MSA were highly accurate, with a negligible difference compared to the ground truth alignments computed by Math. Viral MSA is an open-source tool that is highly performant and accurate, making it an excellent option for viral analyses. The study was supported by an NSF grant and Google Cloud platform research credits. The speaker also highlighted the developer of minimap2 for their expertise in developing the tool, which enabled Viral MSA's speed and performance.
A video of Niema’s presentation can be found on the CIC website.
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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 the next CIC Lightning Talks webinar! Please sign up for the CIC newsletter to be informed of future CIC events.