An effectiveness study of vaccination and quarantine combination strategies for containing mpox transmission on simulated college campuses.

The ongoing transmission of mpox in specific countries and regions necessitates urgent action. It is essential to implement targeted containment strategies that concentrate on high-risk populations and critical locations, such as college campuses, to effectively curb the spread of mpox. This study is dedicated to evaluating the performance of various vaccination and quarantine strategies in curbing the spread of mpox and estimating the outbreak risk. To accomplish this, we constructed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered (SLIR) model, to examine mpox transmission on a simulated college campus. Our findings reveal that relying solely on PEP is insufficient in containing mpox effectively. To bolster the population immunity and protect the vulnerable, pre-exposure vaccination among high-risk populations prior to an outbreak is imperative. Our study demonstrates that a pre-exposure vaccination rate of 50% in high-risk populations can led to a remarkable 74.2% reduction of infections. This translated to a mere 1.0% cumulative infection incidence in the overall population. In cases where the desired vaccination coverage is not attainable, enhancing case detection and isolation measures can serve as an effective emergency response to contain mpox outbreaks. For pre-exposure vaccination coverage of 20% or lower, a 40% isolation ratio is necessary to keep the cumulative number of infections in check. However, when the coverage exceeds 30%, a reduced isolation ratio of 20% becomes sufficient to manage the outbreak effectively. These insights underscore the importance of strategic pre-exposure vaccination in conjunction with robust surveillance and isolation protocols to safeguard public health and prevent the escalation of mpox outbreaks.

A Large-Scale Online Survey on Clinical Severity and Associated Risk Factors for SARS-CoV-2 Omicron Infection - China, April-May 2023.

What is already known about this topic?: Individuals who initially contract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lack significant mixed immunity. Therefore, it is crucial to monitor the clinical characteristics and associated factors of these individuals in order to inform policy-making. What is added by this report?: The common symptoms reported were fever, cough, and sore throat. Reinfections and receiving four vaccination doses within a 6-month period were found to be associated with a shorter duration of virus shedding, decreased hospitalization rate, and reduced risk of pneumonia. Individuals aged 60 years and older, as well as those with underlying medical conditions, had a higher risk of developing pneumonia. What are the implication for public health practices?: Online surveys conducted through social media platforms have the potential to complement disease surveillance and data collection efforts. In terms of vaccination prioritization, it is recommended to prioritize older individuals and those with underlying diseases.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Employees' seeking preference towards influenza vaccination in organization: A discrete choice experiment in China.

To clarify the preferences of employees seeking influenza vaccination, a discrete choice experiment aims to understand the essential factors that close the gap between intention and behavior. A total of 866 employees with vaccination willingness willing to participated in a discrete choice experiment (DCE) between October 31st and December 6th, 2022 in China including the following attributes: price, vaccination setting, appointment mode, and service time. The data was analyzed using mixed logit models. Employees from smaller enterprises were more likely to get vaccinated collectively. For employees willing to get the influenza vaccine, 95.08 % of their choice was dominated by price. Employees' behavior varied according to their socioeconomic characteristics. Only female employees strongly favored work-site-based vaccination. Price was the primary factor considered by employees for getting the influenza vaccine. DCE would help to develop influenza vaccination intervention targeted at different groups in future studies.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Real-world effectiveness and safety of goserelin 10.8-mg depot in Chinese patients with localized or locally advanced prostate cancer.

OBJECTIVE: Real-word data on long-acting luteinizing hormone-releasing hormone (LHRH) agonists in Chinese patients with prostate cancer are limited. This study aimed to determine the real-world effectiveness and safety of the LHRH agonist, goserelin, particularly the long-acting 10.8-mg depot formulation, and the follow-up patterns among Chinese prostate cancer patients. METHODS: This was a multicenter, prospective, observational study in hormone treatment-naïve patients with localized or locally advanced prostate cancer who were prescribed goserelin 10.8-mg depot every 12 weeks or 3.6-mg depot every 4 weeks with or without an anti-androgen. The patients had follow-up evaluations for 26 weeks. The primary outcome was the effectiveness of goserelin in reducing serum testosterone and prostate-specific antigen (PSA) levels. The secondary outcomes included testosterone and PSA levels, attainment of chemical castration (serum testosterone <50 ng/dL), and goserelin safety. The exploratory outcome was the monitoring pattern for serum testosterone and PSA. All analyses were descriptive. RESULTS: Between September 2017 and December 2019, a total of 294 eligible patients received ≥ 1 dose of goserelin; 287 patients (97.6%) were treated with goserelin 10.8-mg depot. At week 24 ± 2, the changes from baseline [standard deviation (95% confidence interval)] in serum testosterone (n = 99) and PSA (n = 131) were -401.0 ng/dL [308.4 ng/dL (-462.5, -339.5 ng/dL)] and -35.4 ng/mL [104.4 ng/mL (-53.5, -17.4 ng/mL)], respectively. Of 112 evaluable patients, 100 (90.2%) achieved a serum testosterone level < 50 ng/dL. Treatment-emergent adverse events (TEAEs) and severe TEAEs occurred in 37.1% and 10.2% of patients, respectively. The mean testing frequency (standard deviation) was 1.6 (1.5) for testosterone and 2.2 (1.6) for PSA. CONCLUSIONS: Goserelin 10.8-mg depot effectively achieved and maintained castration and was well-tolerated in Chinese patients with localized and locally advanced prostate cancer.

Healthcare-seeking behaviours of patients with acute respiratory infection: a cross-sectional survey in a rural area of southwest China.

OBJECTIVES: This study aimed to assess the healthcare-seeking behaviour and related factors of people with acute respiratory symptoms in the rural areas of central and western China to estimate the disease burden of influenza more accurately. DESIGN: Cross-sectional survey. SETTINGS: Fifty-two communities/villages in the Wanzhou District, Chongqing, China, a rural area in southwest China, from May 2022 to July 2022. PARTICIPANTS: The participants were those who had been living in Wanzhou District continuously for more than 6 months and consented to participate. OUTCOME MEASURES: A semistructured questionnaire was used to determine the healthcare-seeking behaviour of participants, and the dichotomous response of 'yes' or 'no' was used to assess whether participants had acute respiratory symptoms and their healthcare-seeking behaviour. RESULTS: Only 50.92% (360 of 707) of the patients with acute respiratory infection visited medical and health institutions for treatment, whereas 49.08% (347 of 707) avoided treatment or opted for self-medication. The primary reason for not seeing a doctor was that patients felt their condition was not serious and visiting a medical facility for treatment was unnecessary. Short distance (87.54%) and reasonable charges (49.48%) were ranked as the most important reasons for choosing treatment at primary medical and health facilities (80.27%). The primary reasons for which patients visited secondary and tertiary hospitals (7.78% and 8.61%, respectively) were that doctors in such facilities were better at diagnosis (57.14%) and at treatment (87.10%). CONCLUSION: The findings provided in this study indicated that regular healthcare-seeking behaviour investigations should be conducted. The disease burden of influenza can be calculated more accurately when healthcare-seeking behaviour investigations are combined with surveillance in the hospitals.

Antibacterial and Angiogenic (2A) Bio-Heterojunctions Facilitate Infectious Ischemic Wound Regeneration via an Endogenous-Exogenous Bistimulatory Strategy.

In infectious ischemic wounds, a lack of blood perfusion significantly worsens microbe-associated infection symptoms and frequently complicates healing. To overcome this daunting issue, antibacterial and angiogenic (2A) bio-heterojunctions (bio-HJs) consisting of CuS/MXene heterojunctions and a vascular endothelial growth factor (VEGF)-mimicking peptide (VMP) are devised and developed to accelerate infectious cutaneous regeneration by boosting angiogenesis via an endogenous-exogenous bistimulatory (EEB) strategy. Assisted by near-infrared irradiation, the bio-HJ platform exhibits versatile synergistic photothermal, photodynamic, and chemodynamic effects for robust antibacterial efficacy. In addition, copper ions liberated from 2A bio-HJs elevate VEGF secretion from fibroblasts, which provokes VEGF receptors (VEGFR) activation through an endogenous pathway, whereas VMP itself promotes an exogenous pathway to facilitate endothelial cell multiplication and tube formation by directly activating the VEGFR signaling pathway. Moreover, employing an in vivo model of infectious ischemic wounds, it is confirmed that the EEB strategy can considerably boost cutaneous regeneration through pathogen elimination, angiogenesis promotion, and collagen deposition. As envisaged, this work leads to the development of a powerful 2A bio-HJ platform that can serve as an effective remedy for bacterial invasion-induced ischemic wounds through the EEB strategy.

Engineered Bio-Heterojunction Confers Extra- and Intracellular Bacterial Ferroptosis and Hunger-Triggered Cell Protection for Diabetic Wound Repair.

Nanomaterial-mediated ferroptosis has garnered considerable interest in the antibacterial field, as it invokes the disequilibrium of ion homeostasis and boosts lipid peroxidation in extra- and intracellular bacteria. However, current ferroptosis-associated antibacterial strategies indiscriminately pose damage to healthy cells, ultimately compromising their biocompatibility. To address this daunting issue, this work has designed a precise ferroptosis bio-heterojunction (F-bio-HJ) consisting of Fe2 O3 , Ti3 C2 -MXene, and glucose oxidase (GOx) to induce extra-intracellular bacteria-targeted ferroptosis for infected diabetic cutaneous regeneration. Fe2 O3 /Ti3 C2 -MXene@GOx (FMG) catalytically generates a considerable amount of ROS which assaults the membrane of extracellular bacteria, facilitating the permeation of synchronously generated Fe2+ /Fe3+ into bacteria under near-infrared (NIR) irradiation, causing planktonic bacterial death via ferroptosis, Fe2+ overload, and lipid peroxidation. Additionally, FMG facilitates intracellular bacterial ferroptosis by transporting Fe2+ into intracellular bacteria via inward ferroportin (FPN). With GOx consuming glucose, FMG creates hunger protection which helps macrophages escape cell ferroptosis by activating the adenosine 5'-monophosphate (AMP) activated protein kinase (AMPK) pathway. In vivo results authenticate that FMG boosts diabetic infectious cutaneous regeneration without triggering ferroptosis in normal cells. As envisaged, the proposed tactic provides a promising approach to combat intractable infections by precisely terminating extra-intracellular infection via steerable ferroptosis, thereby markedly elevating the biocompatibility of therapeutic ferroptosis-mediated strategies.

13-Valent pneumococcal conjugate vaccines vaccination innovative strategy in Weifang City, China: a case study.

The World Health Organization (WHO) prioritizes pneumococcal disease as a vaccine-preventable disease and recommends the inclusion of pneumococcal conjugate vaccines (PCV) in national immunization programs worldwide. However, PCV is not included in the National Immunization Program in China and has low vaccination coverage due to its high cost. To address this, Weifang City implemented an innovative strategy for a 13-valent PCV (PCV13) on June 1, 2021. This strategy aimed to provide one dose of PCV13 free of charge for children aged 6 months to 2 years in registered households and to adopt a commercial insurance model with one dose of PCV13 free of charge in 2023 for children over 2 years old. The Health Commission of Weifang and other departments conducted a comprehensive investigation and considered various factors, such as vaccine effectiveness, safety, accessibility, vaccine price, and immunization schedules, for eligible children (under 5 years old). Stakeholder opinions were also solicited before implementing the policy. The Commission negotiated with various vaccine manufacturers to maximize its negotiating power and reduce vaccine prices. The implementation plan was introduced under the Healthy Weifang Strategy. Following the implementation of this strategy, the full course of vaccination coverage increased significantly from 0.67 to 6.59%. However, vaccination coverage is still lower than that in developed countries. Weifang's PCV13 vaccination innovative strategy is the first of its kind in Chinese mainland and is an active pilot of non-immunization program vaccination strategies. To further promote PCV13 vaccination, Weifang City should continue to implement this strategy and explore appropriate financing channels. Regions with higher levels of economic development can innovate the implementation of vaccine programs, broaden financing channels, improve accessibility to vaccination services, and advocate for more localities to incorporate PCV13 into locally expanded immunization programs or people-benefiting projects. A monitoring and evaluation system should also be established to evaluate implementation effects.

Evaluating the accessibility and capacity of SARS-CoV-2 vaccination and analyzing convenience-related factors during the Omicron variant epidemic in Beijing, China.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination service system lacks standardized indicators to assess resource allocation. Moreover, data on specific vaccination-promoting measures is limited. This study aimed to evaluate vaccination accessibility and capacity and investigate convenience-related factors in China during the Omicron variant epidemic. We collected information on SARS-CoV-2 vaccination services among vaccination sites in Beijing. Analysis was performed using nearest neighbor, Ripley's K, hot spot analysis, and generalized estimating equations. Overall, 299 vaccination sites were included. The demand for the SARS-CoV-2 vaccine increased with the increase in daily new cases, and the number of staff administering vaccines should be increased in urban areas at the beginning of the epidemic. Providing vaccination for both children and adults, extending vaccination service hours, and offering a wider range of vaccine categories significantly increased the doses of vaccines administered (all P < .05). The provision of mobile vaccination vehicles effectively increased the doses of vaccines administered to individuals aged ≥ 60 years (P < .05). The allocation of SARS-CoV-2 vaccination services should be adjusted according to geographic location, population size, and vaccination demands. Simultaneous provision of vaccination services for children and their guardians, flexible service hours, prompt innovative vaccine production, and tailored vaccination strategies can foster vaccination uptake.

Interactions among acute respiratory viruses in Beijing, Chongqing, Guangzhou, and Shanghai, China, 2009-2019.

Background: A viral infection can modify the risk to subsequent viral infections via cross-protective immunity, increased immunopathology, or disease-driven behavioral change. There is limited understanding of virus-virus interactions due to lack of long-term population-level data. Methods: Our study leverages passive surveillance data of 10 human acute respiratory viruses from Beijing, Chongqing, Guangzhou, and Shanghai collected during 2009 to 2019: influenza A and B viruses; respiratory syncytial virus A and B; human parainfluenza virus (HPIV), adenovirus, metapneumovirus (HMPV), coronavirus, bocavirus (HBoV), and rhinovirus (HRV). We used a multivariate Bayesian hierarchical model to evaluate correlations in monthly prevalence of test-positive samples between virus pairs, adjusting for potential confounders. Results: Of 101,643 lab-tested patients, 33,650 tested positive for any acute respiratory virus, and 4,113 were co-infected with multiple viruses. After adjusting for intrinsic seasonality, long-term trends and multiple comparisons, Bayesian multivariate modeling found positive correlations for HPIV/HRV in all cities and for HBoV/HRV and HBoV/HMPV in three cities. Models restricted to children further revealed statistically significant associations for another ten pairs in three of the four cities. In contrast, no consistent correlation across cities was found among adults. Most virus-virus interactions exhibited substantial spatial heterogeneity. Conclusions: There was strong evidence for interactions among common respiratory viruses in highly populated urban settings. Consistent positive interactions across multiple cities were observed in viruses known to typically infect children. Future intervention programs such as development of combination vaccines may consider spatially consistent virus-virus interactions for more effective control.

Association between ozone and influenza transmissibility in China.

BACKGROUND: Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS: We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS: Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS: O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

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.

Increased population susceptibility to seasonal influenza during the COVID-19 pandemic in China and the United States.

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.

Construction of an indicator framework for vaccine inclusion in public health programs: A Delphi-entropy method study.

Governments must decide which vaccine priority to include in their public health programs. Using the modified Delphi and entropy method, we developed an indicator framework for vaccine inclusion at the national, provincial, municipal, and district/county levels, each containing three dimensions. In total, 4 primary indicators, 17 secondary indicators, and 45 tertiary indicators were selected, covering vaccine-preventable diseases, candidate vaccines, and social drivers of the supply and demand sides. From a subjective perspective, there was no significant weighting difference in the primary and secondary indicators at all administrative levels. "Vaccine-preventable diseases" as a primary indicator had the greatest weight in the peer group, of which "Health burden" had the highest weight among the secondary indicators. From the objective perspective, the social drivers on the supply and demand sides of the primary indicators accounted for 65% and higher. Among the secondary indicators, "the characteristics of the candidate vaccine" and "vaccine-related policies on the supply side" held 8% of weights or more at both national and provincial levels. "Demographic characteristics" held the highest weight at the municipal (13.50) and district/county (15.45) level. This study indicates that China needs different considerations when using WHO-recommended vaccines at the national, provincial, municipal, and district/county levels. In addition, this study highlights that behavioral and social drivers are important indicators that need to be considered for decision-making. This framework provides a tool for policymakers to determine the inclusion priority of candidate vaccines.

Clinical manifestations of respiratory syncytial virus infection and the risk of wheezing and recurrent wheezing illness: a systematic review and meta-analysis.

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.