Risk Narratives on Immigration During the COVID-19 Crisis in Italy: A Comparative Analysis of Facebook Posts Published by Politicians and by News media

This work explores how the narrative on immigration changes when society is threatened by "real” risks, i.e., during the COVID-19 health crisis. We compared the frequency and engagement of over 348,684 posts published on Facebook between December 2019 and November 2020 by Italian politicians and news media. We identified two waves of  "tangible crisis” assuming that in these two periods the risk stemming from COVID-19 was strongly perceived by the Italian population, contrasting our observations to the periods preceding the first wave and between both waves. Our findings suggest that the political discourse and risk narratives on immigration decreased during times of "tangible crises” for right-wing populist parties and news media. This happened at a time when key policies regarding immigration and regularization of migrants were highly discussed by the Italian government, receiving unexpectedly low reactions. This leads us to theorize that anti-immigrant communication decreases during times of "tangible crises.”  [ FROM AUTHOR] Copyright of Journal of Communication Inquiry is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.