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The role and functional components of statistical alerting methods for biosurveillance
Published in David L. Blazes, Sheri H. Lewis, Disease Surveillance, 2016
The final subtopic treated here on aggregation and filtering of data sources for surveillance is the application to various forms of social media. The use of social media channels to monitor population health is rapidly growing in multiple directions, not only because of the rapid rise and emerging public health potential of these methods, but also because data sets are less constrained by privacy and proprietary barriers than clinical data sets. Eysenbach introduced the term infodemiology as “the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy.” He included in this broad definition web queries, tweets, health-related Internet sites, and other online evidence sources (Eysenbach 2006), and this expansive concept stimulated work on filtering strategies for many such sources. For example, among many published efforts investigating the utility of web searches, Hulth et al. applied a partial least-squares regression method to find web queries that displayed the same pattern as clinical syndromic indicators during epidemics (Hulth et al. 2009). Twitter data have been the subject of considerable research. For example, Collier et al. classified tweet using support vector machines and Naïve Bayes classifiers based on unigrams, bigrams and, regular expressions (Collier et al. 2011). Sophisticated natural language processing techniques have been applied to long text streams from websites involving public health reports (Collier 2012). For example, Chanlekha et al. developed a linguistics-based spatiotemporal zoning scheme to classify web documents, and validation using interrater agreement from a group of human annotators was promising (Chanlekha and Collier 2010). Far more research on the optimal use of natural language processing for classifying online documents and web searches will likely be available by the time this article is published. As this research corpus grows, the ultimate application and value to public health of these social media evidence sources are still being determined.
The Worldwide Utilization of Online Information about Dementia from 2004 to 2022: An Infodemiological Study of Google and Wikipedia
Published in Issues in Mental Health Nursing, 2023
Exploring online information utilization for dementia can be subsumed under the emerging field of infodemiology. “Infodemiology” or information epidemiology is a subfield of epidemiology, which Eysenbach (2009) defined as the “science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy.” It also utilizes “Big data,” a trend in health informatics that deals with internet usage and search terms (Mondia et al., 2022). Big data analysis can help determine real-time statistics about disease epidemiology, healthcare research, public knowledge, and health-seeking behaviors (Mondia et al., 2022). Infodemiological research and big data analysis have been utilized to understand changes in online health information utilization (Mavragani & Ochoa, 2019; Nuti et al., 2014). Thus, infodemiological research designs can be used to explore online information utilization for dementia.
Infodemic, social contagion and the public health response to COVID-19: insights and lessons from Nigeria
Published in Journal of Communication in Healthcare, 2022
Bridget O. Alichie, Nelson Ediomo-Ubong, Blessing Nonye Onyima
Because of the spread of viral unverifiable news, UNESCO’s handbook of journalism education launched on World Press Freedom Day 2020 has stated that to help tackle the ongoing COVID-19 outbreak, regulating the trend of fake news should be high on the agenda of societies. The Coronavirus outbreak has gone viral on online platforms due to a tsunami of information as a wide variety of commercial, biased, and/or inaccurate sources undermine states' containment efforts [7,19,20]. Appropriate containment strategies that help limit negative public health emergency responses include prioritizing the infodemiology aspects with the epidemiological or clinical models against epidemics. Deployment of infodemiology ensures online surveillance against infodemic during COVID-19 infectious disease outbreak proves crucial in all affected contexts like Nigeria. This owes to the extent to which millions of daily users of online platforms increasingly engage in all health issues specially to curb the spread. Such infodemiology assessment of discourses surrounding disease spread or transmission of specific viral diseases on various social media platforms help to establish the possibility of depoliticizing effects, as well as unraveling the important social, economic and even reproductive aspects integral to the spread of different viral epidemics [21,22].
Prebunking messaging to inoculate against COVID-19 vaccine misinformation: an effective strategy for public health
Published in Journal of Communication in Healthcare, 2022
Maryline Vivion, Elhadji Anassour Laouan Sidi, Cornelia Betsch, Maude Dionne, Eve Dubé, S. Michelle Driedger, Dominique Gagnon, Janice Graham, Devon Greyson, Denis Hamel, Stephan Lewandowsky, Noni MacDonald, Benjamin Malo, Samantha B. Meyer, Philipp Schmid, Audrey Steenbeek, Sander van der Linden, Pierre Verger, Holly O. Witteman, Mushin Yesilada
While vaccine hesitancy was identified as one of the ten threats to global health in 2019 by the WHO [58], the end of the COVID-19 pandemic depends on high worldwide vaccine acceptance to reach global herd immunity. The results of this study provide new insights for public health communication for COVID vaccines and likely for other vaccines as well. Providing prebunking messages based on inoculation theory is an effective strategy to help counter misinformation. As recent studies indicate, simply providing clear and transparent information on safety and efficacy does not increase COVID-19 vaccine intention [59], as public health authorities should include this strategy in their communication practices. In a pandemic and infodemic context where misinformation flows fast, it could be interesting to use social media as it offers the opportunity to quickly spread prebunking messages, as in this study that replicated social media platforms. Social media use would provide the opportunity to share prebunking messages with target groups while on the same ground of misinformation. To be effective, however, prebunking messages must quickly detect misinformation messages that could be harmful and lead to dangerous behaviors. Therefore, to frame prebunking messages, it is crucial for public health authorities to integrate infodemiology and infoveillance expertise to target the more impactful misinformation messages [29, 60].