Risk Factors for Admission into COVID-19 General Wards, Sub-Intensive and Intensive Care Units among SARS-CoV-2 Positive Subjects in the Municipality of Bologna, Italy (preprint)

This is a retrospective cohort study aimed at identifying the risk factors for the hospitalization of patients with COVID-19 in the municipality of Bologna. A total of 32500 patients that tested positive for COVID-19 from February 28/2020 to October 13/2021 in the municipality of Bologna were included. The Kaplan-Meier method was used to estimate changes during time of ICU hospitalization for all patients as well as stratifying subjects by sex. A multi-state Cox's proportional hazard model was fitted to investigate predictors of ICU and non-ICU hospitalization. Age, sex, calendar period of diagnosis, comorbidities and vaccination status of patients at the time of diagnosis were considered as candidate predictors. In general, male sex and advanced age resulted to be poor prognostic factors of COVID-19 outcomes. An exception was found for the over 80 age group which showed a decrease in the risk of ICU hospitalization compared to 70-79 (HR 0.57 95% CI 0.36 - 0.90 for DIAG->ICU; HR 0.40 95% CI 0.28 - 0.58 for HOSP->ICU). Having contracted the disease during the first wave was associated with a significant greater risk of hospitalization than during the second wave, while no difference in the risk of ICU admission was found between the second and third waves. Fully immunized patients showed a significant decrease in the risk of ICU and non-ICU hospitalization compared to the unvaccinated patients (HR 0.23 95% CI 0.16 - 0.31 for DIAG->HOSP; HR 0.10 95% CI 0.01 - 0.73 for DIAG->ICU). Chronic kidney failure and asthma were risk factors for non-ICU hospitalization. Diabetes and embolism were risk factors for both direct ICU and non-ICU hospitalization. The study of factors associated with a negative course of the COVID-19 disease allows to prevent fatal outcomes, establish priorities in the treatment of the disease and improve the management of hospital resources and the pandemic itself.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic (preprint)

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.