Mapping obesity and diabetes' representation on Twitter: the case of Italy.

One of the main functions of public health is to monitor population health to identify health problems and priorities. Social media is increasingly being used to promote it. This study aims to investigate the field of diabetes and obesity and related tweets in the context of health and disease. The database extracted using academic APIs (Application Programming Interfaces) allowed the study to be run with content analysis and sentiment analysis techniques. These two analysis techniques are some of the tools of choice for the intended objectives. Content analysis facilitated the representation of a concept and a connection between two or more concepts, such as diabetes and obesity, on a purely text-based social platform such as Twitter. Sentiment analysis therefore allowed us to explore the emotional aspect related to the collected data related to the representation of such concepts. The results show a variety of representations connected to the two concepts and their correlations. From them it was possible to produce some clusters of elementary contexts and structure narrative and representational dimensions of the investigated concepts. The use of sentiment analysis and content analysis and cluster output to represent complex contexts such as diabetes and obesity for a social media community could increase knowledge of how virtual platforms impact fragile categories, facilitating concrete spillovers into public health strategies.

Drawing a pandemic vulnerabilities' map: The SoNAR-global Vulnerabilities Assessment digital and its output.

This paper describes the process, advantages and limitations of a qualitative methodology for defining and analyzing vulnerabilities during the COVID-19 pandemic. Implemented in Italy in two sites (Rome and outside Rome, in some small-medium sized municipalities in Latium) in 2021, this investigation employed a mixed digital research tool that was also used simultaneously in four other European countries. Its digital nature encompasses both processes of data collection. Among the most salient is that the pandemic catalyzed new vulnerabilities in addition to exacerbating old ones, particularly economic. Many of the vulnerabilities detected, in fact, are linked to previous situations, such as the uncertainties of labor markets, having in COVID-19 to the greatest negative effects on the most precarious workers (non-regular, part-time, and seasonal). The consequences of the pandemic are also reflected in other forms of vulnerability that appear less obvious, having exacerbated social isolation, not only out of fear of contagion, but because of the psychological challenges posed by containment measures themselves. These measures created not mere discomfort, but behavioral changes characterized by anxiety, fearfulness, and disorientation. More generally, this investigation reveals the strong influence of social determinants throughout the COVID-19 pandemic, creating new forms of vulnerability, as the effects of social, economic, and biological risk factors were compounded, in particular, among already marginalized populations.

Territorial spillover of COVID-19 infections in Rome during the "second wave".

The study investigates the spread of the effects of COVID in 2019 in the city of Rome, focusing on the socio-economic factors that affect the incidence of the virus in the 155 urban areas (UAs) of the city. The units of analysis of this study are the UAs. The survey emphasizes the weight of spatial contiguity between the 155 UAs. For this purpose, the spatial data model analyses the spillover between contiguous units of analysis, distinguishing direct and indirect spatial effects. Digital geocoding of the collected data has been performed to create a geodatabase (GDB) that allows the statistical information to be turned into geographic layers. Geographic layers represent information layers that can be overlapped with each other on the map of Rome. The database allowed the variables to be handled with spatial analysis methods. This emphasizes the usefulness of digital analysis methods for the study of such a complex and rapidly changing phenomenon as the spread of SARS-CoV-19 infection on an urban scale.