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SARS-CoV-2 is a highly contagious virus that can affect almost any system in the body. New developments in understanding its transmissibility, management, and sequelae are unfolding almost daily. However, no medical publication in 2021 would be complete without a snapshot of the current status of this pandemic. The virus continues to mutate to more contagious, and therefore more dangerous, strains. The best path forward through this pandemic is vaccination against SARS-CoV-2 for all those who are eligible. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Background. COVID-19 monoclonal antibodies (mAb) have been shown to reduce risk of ED visit and hospitalization when administered early in disease course. However, there has been little data evaluating the challenges and barriers to delivering an outpatient mAb infusion. Methods. This was a retrospective cohort review of patients referred 12/7/20-5/ 20/21 to evaluate factors associated with selection and treatment with mAb. Inclusion criteria included symptom onset within 10 days of referral, high-risk for severe disease, not hospitalized or meeting hospitalization criteria, and followed by in-network primary care provider. We collected data on demographics, high risk comorbidities, illness course, infusion selection, and adverse events. Using unadjusted regression models, we estimated effect sizes (mean difference or percentage point difference) and 95% confidence intervals to estimate baseline factors associated with infusion. Results. Of the 230 eligible subjects, 179 (78%) were selected for mAb treatment and 119 (66%) were infused. Those with a Social Vulnerability Index > 0.5 were more likely to be selected (86% vs 71%) but among those selected were less likely to be infused (59% vs 74%). Other predictors for selection (Fig 1) included Spanish language preference, 3+ symptoms at the time of referral, shortness of breath, headache, immunosuppressive medications, and chronic respiratory disease. Predictors of mAb infusion (Fig 2) included 3+ symptoms at time of referral, diarrhea, myalgias, male gender, being employed, and history of chronic kidney or respiratory disease. Time of onset of symptoms-to-referral was < 5 days and test-to-referral was 2-3 days (Fig 3). 60 subjects were selected but not infused. 19 (32%) were not infused due to becoming ineligible after referral. Reasons for ineligibility included clinical decompensation or passing eligibility window. 39 (65%) were not infused due to refusal after selection. Most common reasons for refusal included feeling better (12, 20%), concerns about mAb (6, 10%), and lack of caregiver for a family member (4, 7%). (Figure Presented) Conclusion. Our study identified factors that impact selection and infusion of mAb in a real-world setting. This can help address potential barriers in operationalizing the administration of COVID-19 mAb in the future.
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The COVID-19 pandemic has been accompanied by a flood of misinformation on social media, which has been labeled an "infodemic". While a large part of such fake news is ultimately inconsequential, some of it has the potential to real-world harm, but due to the massive amount of social media contents, it is impossible to find this misinformation manually. Thus, conventional fact-checking can typically only counteract misinformation narratives after they have gained significant traction. Only automated systems can provide warnings in advance. However, the automatic detection of misinformation narratives is very challenging since the texts that spread misinformation may be short messages on Twitter. They may also transmit misinformation by implication rather than by stating counterfactual information outright, and satirical messages complicate the issue further. Thus, there is a need for highly sophisticated detection systems. In order to support their development, we created substantial ground truth data by human annotation. In this paper, we present a dataset that deals with a specific piece of misinformation: the idea that the COVID-19 pandemic is causally connected to the 5G wireless network. We selected more than 10,000 tweets that deal with COVID-19 and 5G and labeled them manually, distinguishing between tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and tweets that do neither. We provide the human-annotated dataset along with an additional large-scale automatically (by using the human-annotated dataset as the training set) labelled dataset consist of more than 100,000 tweets. © 2021 ACM.
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The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artificial intelligence, and in particular deep learning, has enabled researchers to gain new insights from large scale and multimodal data. At the 2022 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare", we showcase the latest research, influenced and inspired by the idea of using technology to build a more fair, tailored, and cost-effective healthcare system after the COVID-19 pandemic.