Background: Respiratory syncytial virus (RSV) infection in infants is a global health priority. We aimed to investigate the common manifestations of RSV infection by age group and human development index (HDI) level and to assess its association with the development of wheezing and recurrent wheezing illness. Methods: We searched the literature published between January 1, 2010 and June 2, 2022 in seven databases. Outcomes included common manifestations and long-term respiratory outcomes of RSV infection in children. Random- and fixed-effect models were used to estimate the effect size and their 95% confidence intervals. Subgroup analysis was conducted by age and HDI levels. This review was registered in PROSPERO (CRD42022379401). Results: The meta-analysis included 47 studies. The top five manifestations were cough (92%), nasal congestion (58%), rhinorrhea (53%), shortness of breath (50%), and dyspnea (47%). The clinical symptoms were most severe in infants. In our analysis, compared to very high and high HDI countries, fewer studies in medium HDI countries reported related manifestations, and no study in low HDI countries reported that. The RSV-infected infants were more likely to develop wheezing than the non-infected infants [odds ratio (OR), 3.12; 95% CI, 2.59–3.76] and had a higher risk of developing wheezing illnesses after recovery (OR, 2.60; 95% CI, 2.51–2.70). Conclusions: Cough and shortness of breath are common manifestations of RSV infection. More attention should be given to infants and areas with low HDI levels. The current findings confirm an association between RSV infection and wheezing or recurrent wheezing illness.

Background: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. Objective: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. Methods: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. Results: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. Conclusions: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

To the best of our knowledge, no previous study has quantitatively estimated the dynamics and cumulative susceptibility to influenza infections after the widespread lifting of COVID‐19 public health measures.We constructed an imitated stochastic susceptible‐infected‐removed model using particle‐filtered Markov Chain Monte Carlo sampling to estimate the time‐dependent reproduction number of influenza based on influenza surveillance data in southern China, northern China, and the United States during the 2022–2023 season. We compared these estimates to those from 2011 to 2019 seasons without strong social distancing interventions to determine cumulative susceptibility during COVID‐19 restrictions.Compared to the 2011–2019 seasons without a strong intervention with social measures, the 2022–2023 influenza season length was 45.0%, 47.1%, and 57.1% shorter in southern China, northern China, and the United States, respectively, corresponding to an 140.1%, 74.8%, and 50.9% increase in scale of influenza infections, and a 60.3%, 72.9%, and 45.1% increase in population susceptibility to influenza.Large and high‐intensity influenza epidemics occurred in China and the United States in 2022–2023. Population susceptibility increased in 2019–2022, especially in China. We recommend promoting influenza vaccination, taking personal prevention actions on at‐risk populations, and monitoring changes in the dynamic levels of influenza and other respiratory infections to prevent potential outbreaks in the coming influenza season.

Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.