Content analysis and characterization of medical tweets during the early Covid-19 pandemic (preprint)

ObjectiveThe novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19 related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This study provides a detailed content and contributor analysis of Covid-19 related tweets among the FOAM community. Design, Setting, ParticipantsTwitter was searched from November 1st, 2019 to March 21st, 2020 for English tweets discussing Covid-19 in the FOAM community. Tweets were classified into one of 13 pre-specified content categories: original research, editorials, FOAM resource, public health, podcast or video, learned experience, refuting false information, policy discussion, emotional impact, blatantly false information, other Covid-19, and non-Covid-19. Further analysis of linked original research and FOAM resources was performed. 1000 randomly selected contributor profiles and those deemed to have contributed false information were analyzed. ResultsThe search yielded 8541 original tweets from 4104 contributors. The number of tweets in each content category were: 1557 other Covid-19 (18{middle dot}2%), 1190 emotional impact (13{middle dot}9%), 1122 FOAM resources (13{middle dot}1%), 1111 policy discussion (13{middle dot}0%), 928 advice (10{middle dot}9%), 873 learned experience (10{middle dot}2%), 424 non-Covid-19 (5{middle dot}0%), 410 podcast or video (4{middle dot}8%), 304 editorials (3{middle dot}6%), 275 original research (3{middle dot}2%), 245 public health (2{middle dot}9%), 83 refuting false information (1{middle dot}0%), and 19 blatantly false (0{middle dot}2%). ConclusionsEarly in the Covid-19 pandemic, the FOAM community used Twitter to share Covid-19 learned experiences, online resources, crowd-sourced advice, research, and to discuss the emotional impact of Covid-19. Twitter also provided a forum for post-publication peer review of new research. Sharing blatantly false information within this community was infrequent. This study highlights several potential benefits from engaging with the FOAM community on Twitter.

Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA) (preprint)

Since the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year. SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.