Epidemiology of influenza in hospitalized children with respiratory tract infection in Suzhou area from 2016 to 2019

Immunogenicity and safety of live attenuated influenza vaccine in children aged 3-17 years in China

(1) Background: The administration of a live attenuated influenza vaccine (LAIV) has emerged as a viable option for preventing pediatric infections. The LAIV vaccine is available in China based on efficacy results. However, LAIV immunogenicity in children aged 3-17 years old in China has not yet to be studied and reported broadly. (2) Methods: This is a substudy investigating the immunogenicity and safety of the LAIV under a Phase 3, multicentre, randomized, double-blind, placebo-controlled trial. A total of 3000 participants were enrolled in a randomized, double-blind, placebo-controlled trial, split in half between vaccine and placebo, was conducted to evaluate a single LAIV dose in this age group. Hemagglutination inhibition (HI) antibody titers and incidence of adverse events were used to evaluate immunogenicity and safety, respectively. (3) Results: Although there was no significant difference in frequencies of all solicited or unsolicited AEs, nasal congestion, headache, and muscle pain were statistically significantly more frequent in vaccine recipients as compared to placebo Seroconversions and geometric mean fold increases in HI antibody titers against all strains were significantly higher in the vaccine group than in the placebo group. (4) Conclusions: The LAIV is safe and immunogenic in Chinese children and adolescents.

Viral respiratory infections in a rapidly changing climate: the need to prepare for the next pandemic

Viral respiratory infections (VRIs) cause seasonal epidemics and pandemics, with their transmission influenced by climate conditions. Despite the risks posed by novel VRIs, the relationships between climate change and VRIs remain poorly understood. In this review, we synthesized existing literature to explore the connections between changes in meteorological conditions, extreme weather events, long-term climate warming, and seasonal outbreaks, epidemics, and pandemics of VRIs from an interdisciplinary perspective. We proposed a comprehensive conceptual framework highlighting the potential biological, socioeconomic, and ecological mechanisms underlying the impact of climate change on VRIs. Our findings suggested that climate change increases the risk of VRI emergence and transmission by affecting the biology of viruses, host susceptibility, human behavior, and environmental conditions of both society and ecosystems. Further interdisciplinary research is needed to address the dual challenge of climate change and pandemics.

Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)₅₂ model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.