Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Influenza burden in children newborn to eleven months of age in a pediatric emergency department during the peak of an influenza epidemic.

BACKGROUND: The aim of this study was to determine the burden of influenza-related diseases in children 0 to 11 months of age during the peak of the 2001 to 2002 influenza epidemic. METHODS: This was a prospective study at the Pediatric Emergency Department of Edouard Herriot tertiary teaching hospital in Lyon, France. The study included 304 infants 0 to 11 months of age. Consecutive patients were systematically enrolled during the 4 weeks of the influenza epidemic peak (Weeks 3 to 6, 2002). Influenza viruses were detected by antigen detection and virus culture from nasal swabs. Structured telephone interviews were conducted on Days 8 and 15 after virus detection. There was also a 6-month survey into the medicoadministrative database to detect late complications that required delayed hospitalization of influenza-positive children. RESULTS: Influenza virus was detected in 99 (33%) of 304 patients (A/H3N2 in 30% and B in 3%). Nonrespiratory symptoms were the dominant clinical manifestations in 30% of influenza-positive children. One child with influenza presented with febrile seizures. Twenty (20%) children with influenza were hospitalized. Parents reported recovery from the illness in 63 and 94% of children on Days 8 and 15, respectively. The median length of an influenza episode was 8 days. CONCLUSIONS: Our results confirm the high prevalence of influenza-related morbidity in infants during the epidemic peak. One child in three consulting to the pediatric emergency room had a virologically confirmed influenza infection regardless of the body temperature. Every fifth child with influenza was admitted to hospital, which corresponds to an admission rate of 237 per 100 000 children 0 to 11 months of age

Application of a forecasting model to mitigate the consequences of unexpected RSV surge: Experience from the post-COVID-19 2021/22 winter season in a major metropolitan centre, Lyon, France.

Background The emergence of COVID-19 triggered the massive implementation of non-pharmaceutical interventions (NPI) which impacted the circulation of respiratory syncytial virus (RSV) during the 2020/2021 season. Methods A time-series susceptible-infected-recovered (TSIR) model was used early September 2021 to forecast the implications of this disruption on the future 2021/2022 RSV epidemic in Lyon urban population. Results When compared to observed hospital-confirmed cases, the model successfully captured the early start, peak timing, and end of the 2021/2022 RSV epidemic. These simulations, added to other streams of surveillance data, shared and discussed among the local field experts were of great value to mitigate the consequences of this atypical RSV outbreak on our hospital paediatric department. Conclusions TSIR model, fitted to local hospital data covering large urban areas, can produce plausible post-COVID-19 RSV simulations. Collaborations between modellers and hospital management (who are both model users and data providers) should be encouraged in order to validate the use of dynamical models to timely allocate hospital resources to the future RSV epidemics