Covid-19 in the news this week
- SportsProMedia: Asian Games 2022 postponed due to Covid-19
- Biospace: Study: Inflammatory Proteins From COVID-19 May Cause Long COIVD
- Forbes: Pfizer’s Covid Vaccine Protection Against Omicron Fades Just Weeks After Second And Third Doses, Study Finds
- Pfizer: Pfizer and BioNTech Provide Update on COVID-19 Vaccine Supply Agreement with European Commission
- CNBC: U.S. licenses key Covid vaccine technology to WHO so other countries can develop shots
- Helio: Moderna’s COVID-19 vaccine safe, effective in children aged 6 to 11 years
- Reuters: Moderna completes FDA submission for use of COVID shot in adolescents, kids
- BreakingNews IE:Europe to drop mandate for face masks during air travel next week
- BioSpace: FDA Approves Lilly and Incyte’s Olumiant For Hospitalized COVID-19 Patients
- ABC News: ‘Unthinkable tragedy’: U.S. COVID-19 death toll surpasses 1 million
- ABC News: WHO: COVID-19 falling everywhere, except Americas and Africa
- University of Colorado: Alarming Increase in Tuberculosis Deaths Emerging in COVID’s Wake
- NWA Online: Covid-19 testing declines globally
- NewsMedical:Remdesivir shows no significant effect on patients with COVID-19 in Solidarity trial
DrugVisual Covid-19 links
Net daily sentiment ranged from -24.89 for Hydroxychloroquine on Thursday the 12th to 28.1 for Moderna vaccine on Wednesday the 11th.
The sentiment for each tweet is scored from -1 (most negative) to +1 (most positive) using VADER sentiment analysis. Net sentiment is calculated by summing the sentiment across all tweets for a given day and/or category, then normalizing the score by the number of tweets.
Medical conditions from the MedDRA dictionary
Infection was the most frequently observed medical condition mentioned. Coldness, Cold had the highest overall net sentiment of 3.38. Heart attack had the lowest net sentiment this week (-72.84).
MedDra is a standardized medical terminology developed by the International Council for Harmonization Cross-referencing tweets against this list is a starting point for identifying medical conditions mentioned in tweets.
Cross-referencing the MedDRA dictionary by sentiment and topic
There were 191 tweets with an strong positive sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) Infection, (2) Sickness, (3) Blindness, Blind, (4) Hepatitis, (5) Cancer, (6) Coldness, Cold, (7) Forgetfulness, (8) Fall, (9) Flu, (10) MS. Of these terms, Forgetfulness (n=7), Fall (n=6), MS (n=6) were not in the top 10 most frequent terms across all tweets.
There were 343 tweets with an strong negative sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) Infection, (2) Sickness, (3) Poisoning, (4) Flu, (5) Cancer, (6) Heart attack, (7) Clotting disorder, (8) Blindness, Blind, (9) Pain, (10) Thrombosis. Of these terms, Pain (n=12), Thrombosis (n=12) were not in the top 10 most frequent terms across all tweets.
The 25 most important words within positive tweets (compared to negative and neutral) tweets are shown in the treemap below. The size of each box represents the weighted score of each word. The word “nyt” within the search for “Moderna vaccine” had the highest overall weight. When the words are summed for each topic, Moderna vaccine had the highest overall weight.
The 25 most important words within negative tweets (compared to positive and neutral) tweets are shown in the treemap below. The word “die” within the search for “Pfizer vaccine” had the highest overall weight. When the words are summed for each topic, “Pfizer vaccine” had the highest overall weight within negative tweets.
This analysis of words evaluates the stemmed version of words using the Snowball algorithm. By stemming words, words with similar meaning, such as pain, painful & pained, are grouped together as simply “pain”.
Webpage created in R version 4.1.0 (2021-05-18) and R Studio (Version 1.4.1717) using the following packages: plotly, kableExtra, formattable, treemap, and wordpressr.
- C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
- Hao Zhu (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4.
- Kun Ren and Kenton Russell (2021). formattable: Create ‘Formattable’ Data Structures. R package version 0.2.1.
- Martijn Tennekes (2021). treemap: Treemap Visualization. R package version 2.4-3.
- Simit Patel (2021). wordpressr: An API Wrapper for WordPress Site APIs. R package version 0.1.0.
- WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/ (last cited: May 18, 2022).
- Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.