Public perception of COVID-19 vaccines through analysis of Twitter content and users

Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control

PURPOSE OF REVIEW

This review examines the current state and future prospects of machine learning (ML) in infection prevention and control (IPC) and antimicrobial stewardship (ASP), highlighting its potential to transform healthcare practices by enhancing the precision, efficiency, and effectiveness of interventions against infections and antimicrobial resistance.

RECENT FINDINGS

ML has shown promise in improving surveillance and detection of infections, predicting infection risk, and optimizing antimicrobial use through the development of predictive analytics, natural language processing, and personalized medicine approaches. However, challenges remain, including issues related to data quality, model interpretability, ethical considerations, and integration into clinical workflows.

SUMMARY

Despite these challenges, the future of ML in IPC and ASP is promising, with interdisciplinary collaboration identified as a key factor in overcoming existing barriers. ML's role in advancing personalized medicine, real-time disease monitoring, and effective IPC and ASP strategies signifies a pivotal shift towards safer, more efficient healthcare environments and improved patient care in the face of global antimicrobial resistance challenges.

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Introduction

To protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.

Methods

In this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.

Results

The results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.

Discussion

While much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public's stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.

Manifestation of Health Denialism in Attitudes toward COVID-19 Vaccination: A Qualitative Study

Science denialism is characterized by the refusal to accept existing consensus and available evidence. Typical strategies denialists employ include spreading conspiracies, selective use of information, relying on fake experts, or general fallacies in logic. A flood of misinformation, fake news, and conspiracy theories accompanied the COVID-19 pandemic. Simultaneously, it was a subject of many denialistic opinions, from denying the existence of the epidemic challenge to claims that questioned the safety and effectiveness of the COVID-19 vaccines. This study's main aim was to assess the manifestations of denialism in attitudes toward the preventive measures recommended during the pandemic, with a special focus on vaccination. In-depth interviews were conducted with fifty representatives of the general population, demonstrating diversified opinions about COVID-19 vaccines and other preventive behaviors. The interviews were performed face to face in participants' houses or at other places they identified as convenient. Some of the interviewees preferred to do the interview via teleconference. The interviews were carried out from November 2022 to March 2023. The interviewees were recruited initially by convenience, and in further stages, the snowball technique was used. The interviewees were residents of four main administrative districts in Poland. Out of 50 participants, 26 were males, 29 were between 18-40, 16 were inhabitants of rural areas, and 28 had a university level of education. The interviews were based on a semi-structured guide that addressed, in addition to views about the origin of the new coronavirus, respondents' attitudes toward vaccination and sanitary recommendation, the health status of interviewees, their use of healthcare services, and their health behaviors. The interviews were transcribed and analyzed with MAXQDA Analytics Pro 2022 software (Release 22.7.0). Thematic analysis (TA) was applied to the content generated from the interviews. Based on the uptake of the COVID-19 vaccine, the participants were divided into three groups: unvaccinated, hesitant, and vaccinated (18, 4, and 28 interviewees, respectively). The main themes were established based on the TA of the interviews: attitudes toward COVID-19 vaccination, perception of sources of information, and the origin of the new coronavirus. The first theme decidedly drew the greatest attention of the interviewees. There was also a clear relationship between vaccination status and the presence of denialist thinking among interviewees. Interestingly, the role of experts as a key source of information about the pandemic was underlined by study participants. However, the criteria for being an expert differed. The subject of the origin of a new coronavirus was not interesting to interviewees. The analysis of the adherence to preventive measures revealed an interplay of diversified attitudes and motivations. Individuals presenting denialist views most frequently abstained from COVID-19 vaccination. However, such views were also present among those who hesitated or even among those who had been vaccinated. Furthermore, denialism was only one of the determinants of adherence to preventive measures.

Twitter discussions on breastfeeding during the COVID-19 pandemic

BACKGROUND

Breastfeeding is a critical health intervention in infants. Recent literature reported that the COVID-19 pandemic resulted in significant mental health issues in pregnant and breastfeeding women due to social isolation and lack of direct professional support. These maternal mental health issues affected infant nutrition and decreased breastfeeding rates during COVID-19. Twitter, a popular social media platform, can provide insight into public perceptions and sentiment about various health-related topics. With evidence of significant mental health issues among women during the COVID-19 pandemic, the perception of infant nutrition, specifically breastfeeding, remains unknown.

METHODS

We aimed to understand public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic through Twitter analysis using natural language processing techniques. We collected and analyzed tweets related to breastfeeding and COVID-19 during the pandemic from January 2020 to May 2022. We used Python software (v3.9.0) for all data processing and analyses. We performed sentiment and emotion analysis of the tweets using natural language processing libraries and topic modeling using an unsupervised machine-learning algorithm.

RESULTS

We analyzed 40,628 tweets related to breastfeeding and COVID-19 generated by 28,216 users. Emotion analysis revealed predominantly "Positive emotions" regarding breastfeeding, comprising 72% of tweets. The overall tweet sentiment was positive, with a mean weekly sentiment of 0.25 throughout, and was affected by external events. Topic modeling revealed six significant themes related to breastfeeding and COVID-19. Passive immunity through breastfeeding after maternal vaccination had the highest mean positive sentiment score of 0.32.

CONCLUSIONS

Our study provides insight into public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic. Contrary to other topics we explored in the context of COVID (e.g., ivermectin, disinformation), we found that breastfeeding had an overall positive sentiment during the pandemic despite the documented rise in mental health challenges in pregnant and breastfeeding mothers. The wide range of topics on Twitter related to breastfeeding provides an opportunity for active engagement by the medical community and timely dissemination of advice, support, and guidance. Future studies should leverage social media analysis to gain real-time insight into public health topics of importance in child health and apply targeted interventions.