Timeliness of online COVID-19 reports from official sources

Abstract

IntroductionMaking epidemiological indicators for COVID-19 publicly available through websites and social media can support public health experts in the near-real-time monitoring of the situation worldwide, and in the establishment of rapid response and public health measures to reduce the consequences of the pandemic. Little is known, however, about the timeliness of such sources. Here, we assess the timeliness of official public COVID-19 sources for the WHO regions of Europe and Africa.MethodsWe monitored official websites and social media accounts for updates and calculated the time difference between daily updates on COVID-19 cases. We covered a time period of 52 days and a geographic range of 62 countries, 28 from the WHO African region and 34 from the WHO European region.ResultsThe most prevalent categories were social media updates only (no website reporting) in the WHO African region (32.7% of the 1,092 entries), and updates in both social media and websites in the WHO European region (51.9% of the 884 entries for EU/EEA countries, and 73.3% of the 884 entries for non-EU/EEA countries), showing an overall clear tendency in using social media as an official source to report on COVID-19 indicators. We further show that the time difference for each source group and geographical region were statistically significant in all WHO regions, indicating a tendency to focus on one of the two sources instead of using both as complementary sources.DiscussionPublic health communication via social media platforms has numerous benefits, but it is worthwhile to do it in combination with other, more traditional means of communication, such as websites or offline communication.

Use of large language models as a scalable approach to understanding public health discourse

“…We used R 4.1.3 and RStudio version 2023.06.1 for analysing the individual classifications of all tweets [26]. A new dataset was created with the following variables: tweet text, classification of the general sentiment and stance toward vaccination by each expert, agreement (binary variable with 1, if more than three experts labelled equally the tweet for each category, or 0 if it was otherwise), percentage of each class (negative, neutral, and positive) per class (general sentiment and stance towards vaccination), and agreed class (in case of disagreement, we selected the class "neutral").…”

“…We used Jupyter Notebook 6.5.2 and Python 3.9.12 to classify the same tweets according to the stance towards vaccination using GPT versions 3.5 (GPT-3.5) and 4 (GPT-4), and Ollama 0.1.17 and Python 3.11.6 to classify the same tweets according to the stance towards vaccination using Mistral and Mixtral. The specific scripts are available in the repository [26]. These LLMs were selected based on its popularity of use (GPT-3.5), expected good performance (GPT-4), and usability with low computing power needed and no cost (Mistral) and expected good performance (Mixtral).…”