Covid-19 in the news this week
- French health body backs new COVID vaccine booster campaign for this autumn
- Pfizer COVID-19 vaccine-associated tinnitus responds to transcranial magnetic stimulation
- U.S. CDC recommends re-isolation if COVID recurs after taking Pfizer’s pill
- AstraZeneca says EU regulator approves COVID shot as booster
- The effectiveness of sotrovimab vs. molnupiravir in preventing severe COVID-19 outcomes in non-hospitalized high-risk COVID-19 adult patients
- COVID-19 may be triggering dramatic hair loss in some people
- U.S. drug regulator lifts clinical hold on Ocugen’s COVID vaccine trial
- FDA panel sets June 15 meeting on Pfizer, Moderna Covid vaccines for infants and toddlers
- What it’s like to have the terrible-taste side effect of Paxlovid, a drug authorized to treat COVID-19
- Biologic medications used by IBD patients boost COVID-19 vaccine response
- Covid can cause ongoing damage to heart, lungs and kidneys, study finds
- French health body backs new COVID vaccine booster campaign for this autumn
- Pfizer COVID19 vaccine-associated tinnitus responds to transcranial magnetic stimulation
- People who rebound with COVID19 after Paxlovid may be highly contagious, new studies suggest
- Repurposed antibiotic may help treat COVID-19
- COVID-19: Amyloids could explain blood clots, neurological symptoms
- Vaccines were not found to protect well against long COVID, study suggests
- COVID-19 booster schedule is needed now, patient safety group says
- Vaccines found not protect well against long COVID
- Paxlovid users experiencing ‘COVID rebound’ should isolate again for 5 days, CDC says
- US Covid Hospitalizations Surge 29% In Two Weeks
- Novavax vaccine poised to be a fourth COVID-19 option in U.S.
DrugVisual Covid-19 links
Daily sentiment
Net daily sentiment ranged from -32.91 for Remdesivir on Thursday the 26th to 49.96 for Moderna vaccine on Wednesday the 25th.
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
Inflammation was the most frequently observed medical condition mentioned. Infection had the highest overall net sentiment of 11.28. Cancer had the lowest net sentiment this week (-51.73).
| medical condition | count | sentiment |
|---|---|---|
| Inflammation | 84 | -46.61 |
| Infection | 75 | 11.28 |
| Blisters, Blister | 51 | -21.34 |
| Sickness | 44 | -40.02 |
| Cancer | 33 | -51.73 |
| Shingles | 32 | 1.97 |
| Flu | 24 | -30.03 |
| Pain | 21 | -35.58 |
| Poisoning | 18 | -47.03 |
| Worry | 17 | 9.27 |
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
Positive tweets:
There were 136 tweets with an strong positive sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) Infection, (2) Shingles, (3) Forgetfulness, (4) Fever, (5) Inflammation, (6) Redness, (7) Blisters, Blister, (8) Worry, (9) Blindness, Blind, (10) Cancer. Of these terms, Forgetfulness (n=8), Fever (n=6), Redness (n=6), Blindness, Blind (n=4) were not in the top 10 most frequent terms across all tweets.
Negative tweets:
There were 294 tweets with an strong negative sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) Inflammation, (2) Sickness, (3) Blisters, Blister, (4) Cancer, (5) Infection, (6) Pain, (7) Poisoning, (8) Flu, (9) Heart attack, (10) Fear. Of these terms, Heart attack (n=9), Fear (n=7) were not in the top 10 most frequent terms across all tweets.
Word-level analysis
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 “biontech” within the search for “Pfizer vaccine” had the highest overall weight. When the words are summed for each topic, Hydroxychloroquine 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”.
References
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: Jun 01, 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.