ABSTRACT
De novo donor-specific antibodies (DSAs) are associated with increased risk of antibody-mediated rejection and worse clinical outcomes after orthotopic heart transplant (OHT). No study has reported the production of DSAs after infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in an OHT population. In this retrospective study, we described coronavirus disease 2019 (COVID-19) incidence and clinical course in a large, contemporary OHT cohort. We showed that the case-fatality rate has significantly decreased since the early days of the pandemic, although remains higher than that of the general population. In addition, we found that 10% of OHT recipients developed de novo DSAs or experienced an increase in pre-existing DSAs after COVID-19, with the majority occurring in unvaccinated patients (15% vs 2%). Further studies are necessary to substantiate our findings in an external cohort.
Subject(s)
COVID-19 , Heart Transplantation , Humans , Isoantibodies , Graft Rejection , Graft Survival , HLA Antigens , Retrospective Studies , SARS-CoV-2 , Transplant RecipientsABSTRACT
The reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.
Subject(s)
Basic Reproduction Number/statistics & numerical data , COVID-19/epidemiology , COVID-19/transmission , Movement , Contact Tracing , Humans , Italy/epidemiology , Models, Theoretical , Physical Distancing , Quarantine , SARS-CoV-2 , United States/epidemiologyABSTRACT
Estimation of the prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of CoViD-19, and for implementing effective public policy intervention strategies. We discuss a simple yet effective approach to estimate the true number of people infected by SARS-CoV-2, using raw epidemiological data reported by official health institutions in the largest EU countries and the USA.