Using geospatial social media data for infectious disease studies: a systematic review

Social Media Behavior Guidelines for Healthcare Professionals: An American Society of Pain and Neuroscience NEURON Project

Purpose

The American Society of Pain and Neuroscience (ASPN) identified a significant gap in resources and guidelines that aim to educate healthcare providers for best practices when engaging on social media. As part of the broader initiatives on Spine and Nerve practice, the executive board of ASPN has decided it would be beneficial to include comprehensive guidance for healthcare providers when engaging on social media.

Methods

A panel of experts was chosen based on expertise, publications, diversity, and their social media presence. Along with expert guidance, the committee conducted an extensive analysis of peer-reviewed literature in communication and medical journals to determine best practices for healthcare practitioners on social media.

Results

Social media messages significantly impact patients' and colleagues' perceptions and actions regarding medical issues. As such, providers and their teams must be aware of legal and ethical considerations in healthcare while maintaining a consistent, educational, and digestible persona online.

Conclusion

The advancement of communication and medical technologies and systems necessitates continued education and resources to adapt to our rapidly changing media and medical landscape.

Parental Hesitancy toward Seasonal Influenza Vaccination for Children under the Age of 18 Years and Its Determinants in the Post-Pandemic Era: A Cross-Sectional Survey among 1175 Parents in China

Children's susceptibility to influenza increased after COVID-19 control measures were lifted. This study investigated parental hesitancy toward seasonal influenza vaccination (SIV) for children and its determinants in the post-pandemic era. An online survey of full-time adult factory workers was conducted in Shenzhen, China in December 2023. This analysis was based on 1175 parents who had at least one child under the age of 18 years. Among all parents, 37.1% were hesitant to have their index child receive SIV. Mothers exhibited lower parental hesitancy toward SIV compared to fathers (31.9% versus 41.3%, p < 0.001). After adjusting for significant background characteristics, mothers and fathers who were more satisfied with the SIV health promotion materials, perceived more severe consequences of seasonal influenza for their children, and perceived more benefits, cues to action, and self-efficacy related to their children's SIV were less likely to exhibit hesitancy toward SIV. Higher frequency of exposure to information about the increasing number of patients or severe cases due to seasonal influenza and other upper respiratory infections on social media was associated with lower parental hesitancy toward SIV among fathers but not mothers. There is a strong need to address parental hesitancy toward SIV for children in the post-pandemic era.

A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment

Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.

Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data

Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.