Correction to: Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy's country reputation and stock market performance.

[This corrects the article DOI: 10.1007/s10260-023-00690-5.].

Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy's country reputation and stock market performance.

During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the first European countries to be severely affected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using different lexicons-based methods, we find a breakpoint corresponding to the date of the first established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country's reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether different machine learning classifiers were able to determine the polarity of tweets posted before and after the outbreak with a different level of accuracy.

Declining Mortality Rate of Hospitalised Patients in the Second Wave of the COVID-19 Epidemics in Italy: Risk Factors and the Age-Specific Patterns.

BACKGROUND: Mortality rate from COVID-19 in Italy is among the world's highest. We aimed to ascertain whether there was any reduction of in-hospital mortality in patients hospitalised for COVID-19 in the second-wave period (October 2020-January 2021) compared to the first one (February-May 2020); further, we verified whether there were clusters of hospitalised patients who particularly benefitted from reduced mortality rate. METHODS: Data collected related to in-patients' demographics, clinical, laboratory, therapies and outcome. Primary end-point was time to in-hospital death. Factors associated were evaluated by uni- and multivariable analyses. A flow diagram was created to determine the rate of in-hospital death according to individual and disease characteristics. RESULTS: A total of 1561 patients were included. The 14-day cumulative incidence of in-hospital death by competing risk regression was of 24.8% (95% CI: 21.3-28.5) and 15.9% (95% CI: 13.7-18.2) in the first and second wave. We observed that the highest relative reduction of death from first to second wave (more than 47%) occurred mainly in the clusters of patients younger than 70 years. CONCLUSIONS: Progress in care and supporting therapies did affect population over 70 years to a lesser extent. Preventive and vaccination campaigns should focus on individuals whose risk of death from COVID-19 remains high.