Prospective serial sampling of 70 patients revealed clinically relevant cycle thresholds (Ct) occurred 9, 26, and 36 days after symptom onset. Race, gender, or corticosteroids did not appear to influence RNA-positivity. Retrospective analysis of 180 patients revealed that initial Ct did not correlate with requirement for admission or intensive care
Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.
We used sentiment analysis and topic modeling to geospatially explore Ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election. All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative. Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.
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