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Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were selected using Spearman correlation coefficient. Secondly, a GWO-LightGBM model was established to obtain the residuals between the predicted and actual values. The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. The incorporation of the Baidu Index was shown to enhance the precision of the proposed model's predictions. The proposed model outperforms comparison models in terms of evaluation metrics such as RMSE and MAPE. Additionally, our study found that the revised Baidu Index indicators show a notable association with ILI trends. [ABSTRACT FROM AUTHOR]

Background: Monitoring how the incidence of influenza infections changes over time is important for quantifying the transmission dynamics and clinical severity of influenza. Infection incidence is difficult to measure directly, and hence, other quantities which are more amenable to surveillance are used to monitor trends in infection levels, with the implicit assumption that they correlate with infection incidence. Methods: Here, we demonstrate, through mathematical reasoning using fundamental mathematical principles, the relationship between the incidence of influenza infections and three commonly reported surveillance indicators: (1) the rate per unit time of influenza‐like illness reported through sentinel healthcare sites, (2) the rate per unit time of laboratory‐confirmed influenza infections and (3) the proportion of laboratory tests positive for influenza ('test‐positive proportion'). Results: Our analysis suggests that none of these ubiquitously reported surveillance indicators are a reliable tool for monitoring influenza incidence. In particular, we highlight how these surveillance indicators can be heavily biassed by the following: the dynamics of circulating pathogens (other than influenza) with similar symptom profiles, changes in testing rates and differences in infection rates, symptom rates and healthcare‐seeking behaviour between age‐groups and through time. We make six practical recommendations to improve the monitoring of influenza infection incidence. The implementation of our recommendations would enable the construction of more interpretable surveillance indicator(s) for influenza from which underlying patterns of infection incidence could be readily monitored. Conclusions: The implementation of all (or a subset) of our recommendations would greatly improve understanding of the transmission dynamics, infection burden and clinical severity of influenza, improving our ability to respond effectively to seasonal epidemics and future pandemics. [ABSTRACT FROM AUTHOR]

Establishing effective mitigation strategies to reduce the spread of influenza virus requires an improved understanding of the mechanisms of transmission. We evaluated the use of a controlled human infection model using an H3N2 seasonal influenza virus to study critical aspects of transmission, including symptom progression and the dynamics of virus shedding. Eight volunteers were challenged with influenza A/Perth/16/2009 (H3N2) virus between July and September 2022 at Emory University Hospital. Viral shedding in the nasopharynx, saliva, stool, urine, and respiratory aerosols was monitored over the quarantine period, and symptoms were tracked until day 15. In addition, environmental swabs were collected from participant rooms to examine fomite contamination, and participant sera were collected to assess seroconversion by hemagglutination inhibition or microneutralization assays. Among the eight participants, influenza virus infection was confirmed in six (75%). Infectious virus or viral RNA was found in multiple physiological compartments, fecal samples, aerosol particles, and on surfaces in the immediate environment. Illness was moderate, with upper respiratory symptoms dominating. In participants with the highest viral loads, antibody titers rose by day 15 post-inoculation, while in participants with low or undetectable viral loads, there was little or no increase in functional antibody titers. These data demonstrate the safety and utility of the human infection model to study features critical to influenza virus transmission dynamics in a controlled manner and will inform the design of future challenge studies focused on modeling and limiting transmission. [ABSTRACT FROM AUTHOR]

Healthy working-age adults are susceptible to illness or caregiving requirements resulting from annual seasonal influenza, leading to considerable societal and economic impacts. The objective of this targeted narrative review is to understand the societal burden of influenza in terms of absenteeism and productivity loss, based on the current literature. This review includes 48 studies on the impact of influenza and influenza-like illness (ILI) and reports on the effect of influenza vaccination, age, disease severity, caring for others, comorbidities, and antiviral prophylaxis on absenteeism and productivity loss due to influenza/ILI, focusing on publications originating from Canada, Europe, and the United States. Influenza/ILI results in substantial work time and productivity loss among working adults and students in Canada, Europe, and the United States, particularly those who are unvaccinated, are <65 years of age, or who have severe disease. Considerable work time and productivity loss is attributable to illness and caregiver burden related to influenza. Further research is required on the impact of influenza on absenteeism and productivity loss in adults with comorbidities to support the development of effective employer policies for working adults with underlying health conditions. [ABSTRACT FROM AUTHOR]

Abstract Multiple respiratory viruses lead to high morbidity and mortality, yet global surveillance platforms focus primarily on seasonal influenza viruses. The COVID-19 pandemic and new RSV vaccines highlight the importance of a broader approach. Upper respiratory tract swabs from children aged 24–59 months presenting with influenza-like illness in The Gambia were collected during follow-up of a live-attenuated influenza vaccine randomised controlled trial in 2017–18. A microfluidic quantitative polymerase chain reaction (qPCR) assay was established and used to detect 21 respiratory viruses. 76.6% of samples had one or more viruses detected (n = 121/158). The viruses detected most frequently were rhinovirus (n = 37/158, 23.4%) and adenovirus (n = 34/158, 21.5%), followed by parainfluenza virus 3, influenza B and human metapneumovirus B. A third of positive samples had multiple viruses detected (two n = 31/121, 25.6%; three n = 9/121, 7.4%). Our data demonstrates how microfluidic qPCR is a useful tool for high-throughput, comprehensive detection of multiple respiratory viruses in surveillance platforms. Rapidly changing epidemiology exemplifies the need for new, broader approaches to virus surveillance in low-resource settings to respond to future epidemics and to guide the need for and use of new prevention and therapeutic measures.

Abstract Background Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

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.

Multiple respiratory viruses lead to high morbidity and mortality, yet global surveillance platforms focus primarily on seasonal influenza viruses. The COVID-19 pandemic and new RSV vaccines highlight the importance of a broader approach. Upper respiratory tract swabs from children aged 24–59 months presenting with influenza-like illness in The Gambia were collected during follow-up of a live-attenuated influenza vaccine randomised controlled trial in 2017–18. A microfluidic quantitative polymerase chain reaction (qPCR) assay was established and used to detect 21 respiratory viruses. 76.6% of samples had one or more viruses detected (n = 121/158). The viruses detected most frequently were rhinovirus (n = 37/158, 23.4%) and adenovirus (n = 34/158, 21.5%), followed by parainfluenza virus 3, influenza B and human metapneumovirus B. A third of positive samples had multiple viruses detected (two n = 31/121, 25.6%; three n = 9/121, 7.4%). Our data demonstrates how microfluidic qPCR is a useful tool for high-throughput, comprehensive detection of multiple respiratory viruses in surveillance platforms. Rapidly changing epidemiology exemplifies the need for new, broader approaches to virus surveillance in low-resource settings to respond to future epidemics and to guide the need for and use of new prevention and therapeutic measures. [ABSTRACT FROM AUTHOR]

Background: Influenza vaccine uptake among healthcare workers is crucial for preventing influenza infections, yet its effectiveness needs further investigation. Objectives: This prospective observational study aimed to assess the protective effect of influenza vaccination among healthcare workers in Shenzhen. Methods: We enrolled 100 participants, with 50 receiving the 2023–2024 quadrivalent influenza vaccine (QIV) and 50 serving as unvaccinated controls. Epidemiological data were collected when the participants presented influenza-like illness. Serum samples were collected at three time points (pre-vaccination and 28 and 180 days after vaccination). Hemagglutination inhibition (HI) assay was performed against the strains included in the 2023–2024 QIV (H1N1, H3N2, BV and BY strains) to assess antibody protection levels. Demographics comparisons revealed no significant differences between the vaccinated and control groups (p > 0.05), ensuring group comparability. Results: The incidence of influenza-like illness was significantly lower in the vaccinated (18%) compared to the control group (36%; p = 0.046; OR = 0.39; 95% CI: 0.15 to 0.98). The vaccinated group also exhibited a higher rate of consecutive two-year vaccinations (48% vs. 24% in the control group, p < 0.05). Additionally, the vaccinated healthcare workers were more inclined to recommend vaccination to their families (80% vs. 48%, p < 0.05). HI titers against H1N1 (p < 0.01), H3N2 (p < 0.01), BV (p < 0.001) and BY (p < 0.01) significantly increased in the vaccinated group at 28 days post-vaccination. Moreover, a marked and sustained increase in HI titers against the H3N2 strain (p < 0.001) was observed at 180 days post-vaccination, highlighting the vaccine's enduring impact on the immune response. The fold change in the HI titers, indicative of the magnitude of the immune response, was significantly higher for H1N1 (p < 0.01), H3N2 (p < 0.001), BV (p < 0.01) and BY (p < 0.05) among the vaccinated individuals compared to the control group, underscoring the vaccine's efficacy in eliciting a robust and sustained antibody response. Conclusion: Influenza vaccination significantly reduces the incidence of influenza-like illness among healthcare workers and promotes a sustained immune response. The study supports the importance of annual vaccination for this group to enhance personal and public health. [ABSTRACT FROM AUTHOR]

Introduction: Severe acute respiratory infection (SARI) is mainly caused by viral pathogens, with a high prevalence in high-risk populations such as infants and older adults. Coinfections by different viruses are frequent and, in some cases, associated with severe disease outcomes. Purpose: The main purpose of this study was to identify respiratory viruses circulating in Barranquilla during the peaks of the COVID-19 pandemic and estimate the prevalence of viral coinfections in samples from individuals with different degrees of respiratory infection. Methods: We received 5083 samples between epidemiological weeks 33–42 of 2021 submitted by the District Health Laboratory of Barranquilla and four local healthcare institutions during COVID-19 surveillance. Among them, we analyzed 101 samples from individuals presenting with influenza-like illness (ILI). Eighteen respiratory viruses, including SARS-CoV-2, were evaluated via qRT-PCR using nasal swabs or nasopharyngeal aspirate samples. Results: Of the 101 study individuals, 56 were male and 45 were female (55.5% and 44.5%, respectively); 25.7% of individuals were infected with at least one of the evaluated viruses. Respiratory syncytial virus (RSV) and human rhinovirus (HRV) were the two most frequently detected viruses (30.7% and 15.4% of total positives, respectively). Coinfections with two or more respiratory viruses accounted for 42% of the total positive cases. Discussion: Our findings indicate the presence of different respiratory viruses in swab or nasopharyngeal aspirate samples from individuals with ILI, including coinfections. These results reveal the circulation of several respiratory viruses in the city of Barranquilla, confirming their importance as potential causes of SARI in Colombia and the need for their active surveillance. [ABSTRACT FROM AUTHOR]

Background: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods: The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results: We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions: Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management. [ABSTRACT FROM AUTHOR]

Background: Although the link between fine particulate matter (PM2.5) and influenza-like illness (ILI) is well established, the effect of the chemical constituents of PM2.5 on ILI remains unclear. This study aims to explore this effect in Guangzhou, China. Methods: Daily data on ILI cases, PM2.5 levels, and specific PM2.5 constituents (black carbon [BC], chlorine [Cl−], ammonia [NH4+], nitrate [NO3−], and sulfate [SO42−]) in Guangzhou, China, were collected for the period of 2014–2019. Additionally, data on gaseous pollutants and meteorological conditions were obtained. By using quasi-Poisson regression models, the association between exposure to PM2.5 and its constituents and ILI risk was estimated. Stratified subgroup analyses were performed by gender, age, and season to explore in depth the effects of these factors on disease risk. Results: Single-pollutant modeling results showed that an increase of one interquartile range (IQR) in Cl−, SO42−, PM2.5, NH4+, BC, and NO3− corresponded to relative risks of ILI of 1.046 (95 % CI: 1.004, 1.090) (lag03), 1.098 (95 % CI: 1.058, 1.139) (lag01), 1.091 (95 % CI: 1.054, 1.130) (lag02), 1.093 (95 % CI: 1.049, 1.138) (lag02), 1.111 (95 % CI: 1.074, 1.150) (lag03), and 1.103 (95 % CI: 1.061, 1.146) (lag03), respectively. Notably, the association between ILI and BC remained significant even after adjusting for PM2.5 mass. Subgroup analyses indicated that individuals aged 5–14 and 15–24 years may exhibit higher sensitivity to BC and Cl− exposure than other individuals. Furthermore, stronger associations were observed during the cold season than during the warm season. Conclusions: Results showed that the mass and constituents of PM2.5 were significantly correlated with ILI. Specifically, the carbonaceous fractions of PM2.5 were found to have a pronounced effect on ILI. These findings underscore the importance of implementing effective measures to reduce the emission of specific sources of PM2.5 constituents to mitigate the risk of ILI. Nevertheless, limitations such as potential exposure misclassification and regional constraints should be considered.

AIM: To investigate the characteristics of the management of ILI in outpatient and inpatient settings in Vietnam. BACKGROUND: Influenza-like illnesses pose a considerable disease burden and antimicrobial resistance (AMR) is a global concern, driven partly by antimicrobial use among ILI cases. METHODS: We conducted a survey among 407 individuals presenting with ILI symptoms at public community health centers and the pediatric ward of a public hospital in Nha Trang city, Khanh Hoa Province, Vietnam, from December 2022 to March 2023. Health-related quality of life (HRQoL) was estimated from the Vietnamese Short Form (SF)-12 questionnaire using the SF-6D algorithm. In addition to descriptive statistics, we conducted multivariable logistic regression analysis to examine the factors associated with antibiotic prescription for outpatient ILI cases. RESULTS: The study enrolled 198 outpatients and 200 inpatients with ILI. Most of the inpatient cases were children under five, and experienced longer illness durations and higher costs, with almost all receiving antibiotics. Antimicrobials were prescribed for 79.3% of outpatients and 99.5% of inpatients. During ILI episodes, HRQoL scores averaged 0.796 (IQR 0.674-0.922) in ≥18 years old. Logistic regression analysis indicated a negative association between a definite diagnosis of viral infection by rapid diagnostic tests and outpatient antibiotic prescription (Odds ratio: 0.20, p value = 0.006). CONCLUSION: This study documents the burden of ILIs in Vietnam, noting a very high proportion of antimicrobial prescribing. Promoting definite diagnosis of viral infections by rapid diagnostic test was suggested to be an effective countermeasure to curtail inappropriate prescription of antimicrobials.

Background COVID-19 may initially manifest as flu-like symptoms. As such, general practitioners (GPs) will likely to play an important role in monitoring the pandemic through syndromic surveillance.Objectives To present a COVID-19 syndromic surveillance tool in Belgian general practices.Methods We performed a nationwide observational prospective study in Belgian general practices. The surveillance tool extracted the daily entries of diagnostic codes for COVID-19 and associated conditions (suspected or confirmed COVID-19, acute respiratory infection and influenza-like illness) from electronic medical records. We calculated the 7-day rolling average for these diagnoses and compared them with data from two other Belgian population-based sources (laboratory-confirmed new COVID-19 cases and hospital admissions for COVID-19), using time series analysis. We also collected data from users and stakeholders about the syndromic surveillance tool and performed a thematic analysis.Results 4773 out of 11,935 practising GPs in Belgium participated in the study. The curve of contacts for suspected COVID-19 followed a similar trend compared with the curves of the official data sources: laboratory-confirmed COVID-19 cases and hospital admissions but with a 10-day delay for the latter. Data were quickly available and useful for decision making, but some technical and methodological components can be improved, such as a greater standardisation between EMR software developers.Conclusion The syndromic surveillance tool for COVID-19 in primary care provides rapidly available data useful in all phases of the COVID-19 pandemic to support data-driven decision-making. Potential enhancements were identified for a prospective surveillance tool.

ABSTRACTDuring the initial half-year of their existence, infants cannot receive the influenza vaccine, yet they face the greatest susceptibility to severe influenza complications. In this study, we seek to determine whether influenza vaccination of maternal and household contacts is associated with a reduced risk of influenza-like illness (ILI) and severe acute respiratory infection (SARI) in infants. This work was prospectively conducted during the influenza season. A total of 206 infants were included in this study. The percentage of infants with only the mother vaccinated is 12.6% (n:26), and the percent of infants with all household contacts vaccinated is 16% (n:33). Among the infants with only the mother vaccinated, the effectiveness of influenza vaccine is estimated as 35.3% for ILI and 41.3% for SARI. Among infants with all household contacts vaccinated, the effectiveness is estimated as 48.9% for ILI and 76.9% for SARI. Based on the results of multivariate logistic regression analysis, all-household vaccination is a protective factor against SARI (OR: 0.07 95% CI [0.01–0.56]), household size (OR: 1.75, 95% CI [1.24–2.48]) and presence of secondhand smoke (OR: 2.2, 95% CI [1.12–4.45]) significant risk factors for SARI in infants. The mother alone being vaccinated is not a statistically significant protective factor against ILI (OR: 0.46, 95% CI [0.19–1.18]) or SARI (OR: 0.3, 95% CI [0.11–1.21]). Along with the obtained results and analysis, this study provides clear evidence that influenza vaccination of all household contacts of infants aged 0–6 months is significantly associated with protecting infants from both ILI and SARI.

In the post-COVID-19 pandemic era, influenza virus infections continuously lead to a global disease burden. Evaluating vaccine effectiveness against influenza infection is crucial to inform vaccine design and vaccination strategy. In this study, we recruited 1120 patients with influenza-like illness (ILI) who attended fever clinics of 4 sentinel hospitals in the Ili Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China, from January 1 to April 7, 2024. Using a test-negative design, we estimated influenza vaccine effectiveness (VE) of 54.7% (95% CrI: 23.7, 73.1) against medical-attended influenza infection, with 62.3% (95% CrI: 29.3, 79.8) against influenza A, and 51.2% (95% CrI: 28.7, 83.0) against influenza B. Despite the moderate VE estimated in this study, influenza vaccination remains the most important approach to prevent influenza at the community level.

Introduction: Measles, a highly transmissible disease marked by fever and a maculopapular rash, posed a substantial threat to life in the 1960s. Nevertheless, the advent of the measles vaccine had a profound impact, significantly diminishing its toll on mortality. Similarly, through effective influenza surveillance and early epidemic warning systems, public health officials can timely identify influenza trends and provide crucial scientific support for prevention and control measures. This proactive approach holds great public health significance. Aim: To analyse the long-term trend of measles cases in Rajasthan, India, and the impact of Coronavirus Disease-2019 (COVID-19) on it, with future predictions using Auto-Regressive Integrated Moving Average (ARIMA) modelling. Materials and Methods: The present study was a retrospective, descriptive observational study in which monthly diagnosed measles cases were collected from the Measles Rubella Laboratory, Department of Microbiology, Sawai Man Singh Medical College, Jaipur, Rajasthan, India, for the period of April 2010 to April 2023. An ARIMA model was developed using data from 2010 to 2020 to predict the monthly number of measles cases in 2021. The predicted values were then compared to the actual cases in 2021 to assess the model’s accuracy. Results: Out of the total positive cases, males were slightly more prone to acquire infection than females (1734 males, 54%; 1477 females, 46%). The monthly index for new measles cases ranged from 0.11 to 2.6. It reached its lowest point in July (0.106) and August (0.25) and peaked in March (2.594) and April (1.84). The overall trend was fluctuating; however, the incidence of measles cases clearly increased after the year 2021. The difference between observed cases and predicted cases for the period of April 2020 to December 2021 was not statistically significant (t-value=0.261 and p-value=0.797). Conclusion: The fluctuating trend of measles was observed during the last decade; however, the observed cases of measles showed an upward trajectory during and after the COVID-19 outbreak. This study also highlighted the monthly index of the measles cases, which peaked in March to April and was lowest in July to August.

ObjectiveTo investigate the epidemiological trends of influenza in Yiwu City, Zhejiang Province from 2017 to 2022, and to provide references for local adjustments to influenza prevention and control strategies.MethodsReal-time fluorescence quantitative PCR was used to detect and type influenza viruses in nasal and pharyngeal swab specimens of the patients with influenza-like illness (ILI). The results were statistically analyzed.ResultsFrom 2017 to 2022, a total of 106 661 ILI cases were reported in Yiwu City, with annual reported cases of 31 273, 33 522, 20 090, 9 965, 3 202, and 8 609, respectively. The majority of ILI cases were in the age group of ≤14 years, accounting for 89.16%. A total of 6 893 specimens were collected and tested, of which 945 were tested positive for influenza virus nucleic acid, with an overall positivity rate of 13.71%. The dominant subtypes were H3N2 and B⁃Victoria, accounting for 40.63% (384/945) and 35.03% (331/945) respectively. The highest positivity rate was in 2019, at 25.19% (265/1 052). The positivity rates significantly decreased in 2020 and 2021, to 5.74% (66/1 149) and 5.77% (75/1 300), respectively. The rate increased in 2022 to 13.85% (180/1 300). The highest proportion of A/H3N2 positivity was in 2017, at 69.14% (168/243). The highest proportion of A/H1N1 positivity was in 2018,at 50.86%(59/116). The highest proportion of B/Victoria positivity was in 2021, at 100.00% (75/75). The highest proportion of B/Yamagata positivity was in 2018, at 5.17% (6/116).ConclusionInfluenza in Yiwu City exhibits obvious seasonal patterns, with two peaks in winter-spring and summer. Dominant strains such as A/H3N2, B/Victoria, and A/H1N1 alternate or co-circulate. During the COVID-19 pandemic, seasonal influenza significantly decreased, and a series of prevention and control measures had a positive effect on influenza prevention and control.

Introduction: Measles, a highly transmissible disease marked by fever and a maculopapular rash, posed a substantial threat to life in the 1960s. Nevertheless, the advent of the measles vaccine had a profound impact, significantly diminishing its toll on mortality. Similarly, through effective influenza surveillance and early epidemic warning systems, public health officials can timely identify influenza trends and provide crucial scientific support for prevention and control measures. This proactive approach holds great public health significance. Aim: To analyse the long-term trend of measles cases in Rajasthan, India, and the impact of Coronavirus Disease-2019 (COVID-19) on it, with future predictions using Auto-Regressive Integrated Moving Average (ARIMA) modelling. Materials and Methods: The present study was a retrospective, descriptive observational study in which monthly diagnosed measles cases were collected from the Measles Rubella Laboratory, Department of Microbiology, Sawai Man Singh Medical College, Jaipur, Rajasthan, India, for the period of April 2010 to April 2023. An ARIMA model was developed using data from 2010 to 2020 to predict the monthly number of measles cases in 2021. The predicted values were then compared to the actual cases in 2021 to assess the model’s accuracy. Results: Out of the total positive cases, males were slightly more prone to acquire infection than females (1734 males, 54%; 1477 females, 46%). The monthly index for new measles cases ranged from 0.11 to 2.6. It reached its lowest point in July (0.106) and August (0.25) and peaked in March (2.594) and April (1.84). The overall trend was fluctuating; however, the incidence of measles cases clearly increased after the year 2021. The difference between observed cases and predicted cases for the period of April 2020 to December 2021 was not statistically significant (t-value=0.261 and p-value=0.797). Conclusion: The fluctuating trend of measles was observed during the last decade; however, the observed cases of measles showed an upward trajectory during and after the COVID-19 outbreak. This study also highlighted the monthly index of the measles cases, which peaked in March to April and was lowest in July to August. [ABSTRACT FROM AUTHOR]

Enterovirus D68 (EV‐D68) is an emerging agent for which data on the susceptible adult population is scarce. We performed a 6‐year analysis of respiratory samples from influenza‐like illness (ILI) admitted during 2014‐2020 in 4‐10 hospitals in the Valencia Region, Spain. EV‐D68 was identified in 68 (3.1%) among 2210 Enterovirus (EV)/Rhinovirus (HRV) positive samples. Phylogeny of 59 VP1 sequences showed isolates from 2014 clustering in B2 (6/12), B1 (5/12), and A2/D1 (1/12) subclades; those from 2015 (n = 1) and 2016 (n = 1) in B3 and A2/D1, respectively; and isolates from 2018 in A2/D3 (42/45), and B3 (3/45). B1 and B2 viruses were mainly detected in children (80% and 67%, respectively); B3 were equally distributed between children and adults; whereas A2/D1 and A2/D3 were observed only in adults. B3 viruses showed up to 16 amino acid changes at predicted antigenic sites. In conclusion, two EV‐D68 epidemics linked to ILI hospitalized cases occurred in the Valencia Region in 2014 and 2018, with three fatal outcomes and one ICU admission. A2/D3 strains from 2018 were associated with severe respiratory infection in adults. Because of the significant impact of non‐polio enteroviruses in ILI and the potential neurotropism, year‐round surveillance in respiratory samples should be pursued. [ABSTRACT FROM AUTHOR]

Background: Influenza is a leading cause of morbidity and mortality globally. Little is known of the true burden and epidemiology of influenza in Africa. Nigeria has a sentinel surveillance system for influenza virus (IFV). This study seeks to describe the epidemiological characteristics of influenza cases in Nigeria through secondary data analysis of the sentinel surveillance data from 2010 to 2020. Methodology: A retrospective secondary data analysis of data collected from patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) in the four Nigeria Influenza Sentinel Surveillance sites from January 2010 to December 2020. Data was cleaned and analyzed using Microsoft Excel and Epi info 7.2 for frequencies and proportions. The results of the analysis were summarized in tables and charts. Results: A total of 13,828 suspected cases of influenza were recorded at the sentinel sites during the study period. About 10.3% (1421/13,828) of these tested positive for IFV of which 1243 (87.5%) were ILI patients, 175 (12.3%) SARI patients, and 3 (0.2%) novel H1N1 patients. Males accounted for 54.2% (770/1421) of the confirmed cases. The median age of confirmed cases was 3 years (range:

Background: A novel coronavirus severe acute respiratory syndrome coronavirus-2 (SARSCoV-2) that emerged in China in December 2019 has spread rapidly globally to many countries including India and World Health Organization declared it as a pandemic on March 11th, 2020. Aims and Objectives: The current study endeavors to determine the SARS-CoV-2 positivity rate as well as epidemiological, clinical, and diagnostic profiles from the second wave. Materials and Methods: We performed a retrospective analysis of all suspected COVID-19 cases from January 2021 to October 2021 presenting at a large testing center for SARS-CoV-2 infection by real-time polymerase chain reaction (RT-PCR). Descriptive analysis has been performed for profiling of clinical-epidemiological aspects of suspected cases. Results: A total of 694427 participants were enrolled during the study from January 2021 to October 2021. Overall RT-PCR positivity rate was found to be 1.7% in the year 2021 and the positivity was maximum in April 2021 which represents the second wave of COVID-19 infection in India. In the study population, more than half (57.07%) of the persons screened for COVID-19 infection were between 21 and 40 years of age, and about two-thirds (65.20%) of the persons screened were male followed by 34.79% were female. Conclusions: SARS-CoV-2 poses a high burden of infections in the community. Males had a higher RT-PCR detection rate as compared to females. The younger age group (80 years) expressed the highest positivity rate.

Abstract Background Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. Methods We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. Results Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. Conclusions Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.

Healthy working-age adults are susceptible to illness or caregiving requirements resulting from annual seasonal influenza, leading to considerable societal and economic impacts. The objective of this targeted narrative review is to understand the societal burden of influenza in terms of absenteeism and productivity loss, based on the current literature. This review includes 48 studies on the impact of influenza and influenza-like illness (ILI) and reports on the effect of influenza vaccination, age, disease severity, caring for others, comorbidities, and antiviral prophylaxis on absenteeism and productivity loss due to influenza/ILI, focusing on publications originating from Canada, Europe, and the United States. Influenza/ILI results in substantial work time and productivity loss among working adults and students in Canada, Europe, and the United States, particularly those who are unvaccinated, are

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences. [ABSTRACT FROM AUTHOR]

Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0–18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU ('non-ICU') versus management requiring ICU care ('ICU') added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction. [ABSTRACT FROM AUTHOR]

Background: Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. Methods: We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. Results: Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. Conclusions: Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity. [ABSTRACT FROM AUTHOR]

Influenza is a leading cause of morbidity and mortality globally. Little is known of the true burden and epidemiology of influenza in Africa. Nigeria has a sentinel surveillance system for influenza virus (IFV). This study seeks to describe the epidemiological characteristics of influenza cases in Nigeria through secondary data analysis of the sentinel surveillance data from 2010 to 2020. A retrospective secondary data analysis of data collected from patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) in the four Nigeria Influenza Sentinel Surveillance sites from January 2010 to December 2020. Data was cleaned and analyzed using Microsoft Excel and Epi info 7.2 for frequencies and proportions. The results of the analysis were summarized in tables and charts. A total of 13,828 suspected cases of influenza were recorded at the sentinel sites during the study period. About 10.3% (1421/13,828) of these tested positive for IFV of which 1243 (87.5%) were ILI patients, 175 (12.3%) SARI patients, and 3 (0.2%) novel H1N1 patients. Males accounted for 54.2% (770/1421) of the confirmed cases. The median age of confirmed cases was 3 years (range: <1month–97 years). Children 0–4 years accounted for 69.3% (985/1421) of all cases. The predominant subtypes were B lineage not determined (32.3%), A/H1N1 pdm09 (28.8%) and A/H3 (23.0%). There were periods of sustained transmission in most years with 2011 having the highest number of cases. Overall, there were more cases around January to March and August to November. Heart disease and chronic shortness of breath were the most common co-morbidities identified among confirmed cases. Influenza remains a significant cause of respiratory illness, especially among children aged less than 4 years. Influenza cases occur all year round with irregular seasonality in Nigeria. Children less than 4 years and those with co-morbidities should be prioritized for vaccination. Vaccine composition in the country should take cognizance of the prevailing strains which are type B (lineage not determined), A/H1N1 pdm09 and A/H3. [ABSTRACT FROM AUTHOR]

Background: A novel coronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV- 2) that emerged in China in December 2019 has spread rapidly globally to many countries including India and World Health Organization declared it as a pandemic on March 11th, 2020. Aims and Objectives: The current study endeavors to determine the SARS-CoV-2 positivity rate as well as epidemiological, clinical, and diagnostic profiles from the second wave. Materials and Methods: We performed a retrospective analysis of all suspected COVID-19 cases from January 2021 to October 2021 presenting at a large testing center for SARS-CoV-2 infection by real-time polymerase chain reaction (RT-PCR). Descriptive analysis has been performed for profiling of clinical-epidemiological aspects of suspected cases. Results: A total of 694427 participants were enrolled during the study from January 2021 to October 2021. Overall RT-PCR positivity rate was found to be 1.7% in the year 2021 and the positivity was maximum in April 2021 which represents the second wave of COVID-19 infection in India. In the study population, more than half (57.07%) of the persons screened for COVID-19 infection were between 21 and 40 years of age, and about two-thirds (65.20%) of the persons screened were male followed by 34.79% were female. Conclusions: SARS-CoV-2 poses a high burden of infections in the community. Males had a higher RT-PCR detection rate as compared to females. The younger age group (<20 years) expressed the least RT-PCR positivity rate and the elderly population (>80 years) expressed the highest positivity rate. [ABSTRACT FROM AUTHOR]

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.

Background: Influenza vaccine uptake among healthcare workers is crucial for preventing influenza infections, yet its effectiveness needs further investigation. Objectives: This prospective observational study aimed to assess the protective effect of influenza vaccination among healthcare workers in Shenzhen. Methods: We enrolled 100 participants, with 50 receiving the 2023–2024 quadrivalent influenza vaccine (QIV) and 50 serving as unvaccinated controls. Epidemiological data were collected when the participants presented influenza-like illness. Serum samples were collected at three time points (pre-vaccination and 28 and 180 days after vaccination). Hemagglutination inhibition (HI) assay was performed against the strains included in the 2023–2024 QIV (H1N1, H3N2, BV and BY strains) to assess antibody protection levels. Demographics comparisons revealed no significant differences between the vaccinated and control groups (p > 0.05), ensuring group comparability. Results: The incidence of influenza-like illness was significantly lower in the vaccinated (18%) compared to the control group (36%; p = 0.046; OR = 0.39; 95% CI: 0.15 to 0.98). The vaccinated group also exhibited a higher rate of consecutive two-year vaccinations (48% vs. 24% in the control group, p < 0.05). Additionally, the vaccinated healthcare workers were more inclined to recommend vaccination to their families (80% vs. 48%, p < 0.05). HI titers against H1N1 (p < 0.01), H3N2 (p < 0.01), BV (p < 0.001) and BY (p < 0.01) significantly increased in the vaccinated group at 28 days post-vaccination. Moreover, a marked and sustained increase in HI titers against the H3N2 strain (p < 0.001) was observed at 180 days post-vaccination, highlighting the vaccine’s enduring impact on the immune response. The fold change in the HI titers, indicative of the magnitude of the immune response, was significantly higher for H1N1 (p < 0.01), H3N2 (p < 0.001), BV (p < 0.01) and BY (p < 0.05) among the vaccinated individuals compared to the control group, underscoring the vaccine’s efficacy in eliciting a robust and sustained antibody response. Conclusion: Influenza vaccination significantly reduces the incidence of influenza-like illness among healthcare workers and promotes a sustained immune response. The study supports the importance of annual vaccination for this group to enhance personal and public health.

Abstract Background Influenza surveillance aims to determine onset, duration and intensity of the seasonal Influence-like Illness (ILI); data collection begins in the week 42 of a year and ends in the week 17 of the following year. In this observational study, we report the experience of a tertiary care children hospital in Rome about Influenza viruses circulation during the calendar year 2022 (January-December) in comparison with the previous five years (2017–2021), with a special focus on the weeks 18–41, usually not under surveillance. Methods This retrospective study involved 36782 respiratory samples referred to 21354 patients (pts), median age 2.63 years, admitted with respiratory symptoms at Bambino Gesù Children’s Hospital in the years 2017–2022. Respiratory viruses were detected by molecular Allplex™ Respiratory Panel Assays (Seegene, Korea). Results Regarding the pre pandemic years, 2017–2019, distribution of Flu positive patients focused in the first weeks of the year (weeks 1–17). During the pandemic period, Flu was not detected. In 2022, 239 Flu viruses were identified: 37 FluA (weeks 1–17), 29 FluA (weeks 18–41) and 168 FluA and 5 FluB (weeks 42–52). For the year 2022, during the non-epidemic period, the number of Flu viruses detected corresponded to 12.1% of total Flu detected, respect to 0-1.7% for the previous five years (p

ABSTRACT: Objectives: Although respiratory syncytial virus (RSV) is a leading cause of acute respiratory infections (ARIs), it is unclear which of the case definitions that prompt swab collection predicts RSV best. We aimed to profile RSV-positive adults and to identify possible RSV case definitions. Methods: This individual-based pooled analysis was based on influenza-like illness (ILI) surveillance conducted among Italian outpatient adults. All samples were tested for influenza, RSV and other respiratory viruses. Results: RSV was detected in 5.2% of the 1240 ILI adults tested. The prevalence of fever/feverishness was significantly lower (83.3%) in individuals positive for RSV and those negative for both viruses (79.4%) than in influenza-positive subjects (96.2%). Conversely, 98.3% of RSV-positive adults reported cough. Compared with subjects who tested negative, the adjusted relative risk ratio of cough in RSV-positive subjects was much higher than in influenza-positive subjects (6.89 vs 2.79). Using ARI with cough as the RSV case definition increased specificity. Conclusion: As fever/feverishness is more common among influenza than RSV cases, ILI-based surveillance may underestimate RSV incidence in adult outpatients. While broad ARI definitions are useful for routine RSV surveillance, their low specificity may hamper vaccine effectiveness studies. The use of further ARI qualifiers like cough increases specificity.

Objective To analyze the results of influenza sentinel surveillance in Sichuan Province from 2020 to 2022, to pro- vide basis for influenza prevention and control in Sichuan. Methods The surveillance data of influenza-like illness (ILI) re- ported by 30 national influenza surveillance sentinel hospitals in Sichuan Province were collected through China Influenza Surveillance Information System, and the results of influenza sentinel surveillance in Sichuan Province from 2020 to 2022 were analyzed retrospectively by descriptive epidemiological methods. Results From 2020 to 2022, there was a correlation be- tween the proportion of ILI to the total number of outpatient and emergency surveillance cases (ILI%) and the detection rate of influenza in ILI samples (r=0.626, P < 0.001). From 2020 to 2022, a total of 103 544 influenza samples were collected and tested in Sichuan Province, of which 8 798 were tested positive, with a positive rate of 8.50%. In 2020, the epidemic level of influenza in Sichuan Province was low, and the epidemic peak was in January, which was co-epidemic of subtype A (H3N2) and strain B (Victoria). In 2021, the epidemic peak was in autumn and winter, and the dominant strain was strain B (Victoria). In 2022, there was an obvious summer epidemic peak in Sichuan Province, and the dominant strain was subtype A (H3N2). There were significant differences in the positive rate of ILI among different years, age groups, and cities and states (P<0.001). Conclusion Influenza B (Yamagata) was not detected in Sichuan Province from 2020 to 2022, and the main epidemic strain were A(H3N2) and B(Victoria). The epidemic strains in Sichuan Province displayed decreased epidemic levels at the end of 2020 and 2022, which may be attributed to the COVID-19 preventive and control efforts and the occurrence of viral interference. It is necessary to continue influenza surveillance and strengthen the prevention and control of influenza in children and students. [ABSTRACT FROM AUTHOR]

Background: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. Objective: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. Methods: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants' steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care–seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F 2) score. Results: An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. Conclusions: Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections. [ABSTRACT FROM AUTHOR]

Aims: Influenza-like illnesses (ILI) affect millions each year in the United States (US). Determining definitively the cause of symptoms is important for patient management. Xpert Xpress CoV-2/Flu/RSV plus (Xpert Xpress) is a rapid, point-of-care (POC), multiplex real-time polymerase chain reaction (RT-PCR) test intended for the simultaneous qualitative detection and differentiation of SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV). The objective of our analysis was to develop a cost-consequence model (CCM) demonstrating the clinico-economic impacts of implementing PCR testing with Xpert Xpress compared to current testing strategies. Methods: A decision tree model, with a 1-year time horizon, was used to compare testing with Xpert Xpress alone to antigen POC testing and send-out PCR strategies in the US outpatient setting from a payer perspective. A hypothetical cohort of 1,000,000 members was modeled, a portion of whom develop symptomatic ILIs and present to an outpatient care facility. Our main outcome measure is cost per correct treatment course. Results: The total cost per correct treatment course was $1,131 for the Xpert Xpress strategy compared with a range of $3,560 to $5,449 in comparators. POC antigen testing strategies cost more, on average, than PCR strategies. Limitations: Simplifying model assumptions were used to allow for modeling ease. In clinical practice, treatment options, costs, and diagnostic test sensitivity and specificity may differ from what is included in the model. Additionally, the most recent incidence and prevalence data was used within the model, which is not reflective of historical averages due to the SARS-CoV-2 pandemic. Conclusion: The Xpert Xpress CoV-2/Flu/RSV plus test allows for rapid and accurate diagnostic results, leading to reductions in testing costs and downstream healthcare resource utilization compared to other testing strategies. Compared to POC antigen testing strategies, PCR strategies were more efficient due to improved diagnostic accuracy and reduced use of confirmatory testing. [ABSTRACT FROM AUTHOR]

Influenza-Like Illness (ILI) constitutes a significant global health concern characterized by its high infection rates and widespread distribution worldwide. While influenza viruses, primarily types A and B, are primary contributors to ILI cases, other respiratory viruses also play a role in its prevalence. Jakarta, Indonesia's largest and densely populated city, has consistently reported a notable weekly number of ILI cases from 2016 to mid-2022. Intriguingly, this pattern of cases is irregular and does not exhibit a direct association with seasonal climate fluctuations. In response to this complex scenario, we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function, obtained from weekly cumulative data to derive model solutions. A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 weeks. Our correlation analysis unveiled distinct temporal relationships within the model, revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment, along with a 2-week lag marking the incubation period between the latent and infected compartments. Furthermore, the effective reproduction number displayed recurrent fluctuations around a threshold of 1, indicating the endemic characteristics where infection persists within the population. This in-depth comprehension of ILI transmission dynamics and effective reproduction numbers plays a significant role in devising control measures and informed policy-making decisions. [ABSTRACT FROM AUTHOR]

The sentinel hospital‐based influenza‐like illness (ILI) surveillance network was established in China since the 2009 H1N1 pandemic. This network plays important roles in monitoring influenza virus variation and identifying novel respiratory pathogens. In this study, we characterized the pathogen spectrum pattern (PSP) of ILI based on three sentinel hospitals and analyzed the significant change of PSP during the COVID‐19 epidemic. The notable change of influenza virus spectrum was observed since the beginning of COVID‐19 outbreak, and we found persistent domination of Victoria lineage of influenza B virus and "extinction" of A/H1N1, A/H3N2, and B/Yamagata during the dynamic Zero‐COVID‐19 pandemic in Nanchang, China. However, these strains intermittently co‐circulated before the COVID‐19. [ABSTRACT FROM AUTHOR]

Abstract Background Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States. Methods ILI, meteorological factors, and PM2.5 of 48 states in the United States were collected during 2011–2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors. Results A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022). Conclusion The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

ObjectiveTo describe epidemiological characteristics of influenza-like illness (ILI) cases reported during 2021 – 22 by sentinel hospitals in Xiamen city, Fujian province for providing evidence to influenza prevention and control. Methods Demographic and medical information for 6 018 ILI cases reported by sentinel hospitals in Xiamen city from August 2021 to July 2022 were collected from National Influenza Surveillance Information System. Epidemiological and pathogenetic characteristics of the ILL cases were analyzed statistically. ResultsOf all the registered ILI cases, 51.99% (3 129) were male and 48.01% (2 889) were female; 49.72% (2 992) were at ages of 3 – 6 years; and 52.64% (3168) were reported during June 2022. For 2 794 pharyngeal swab specimens randomly collected from 46.43% of all cases, 1 683 were positive for influenza virus nucleic acid, with a detection rate of 60.24%. Single-strain infection were detected among most (1 536, 91.27% ) of the cases positive for influenza virus and the ratios of the isolated strains for the single infections were 57.75% for influenza A H3N2, 31.91% for influenza B Victoria lineage, and 1.60% for influenza A H1N1, respectively. The most detected co-infections were caused by influenza B Victoria lineage combined with human metapneumovirus infection, which was identified in 32 cases, accounting for 1.90% of the nucleic acid-positive ILI cases. ConclusionAccording to hospital-based sentinel surveillance, the ILI incidence showed a mono-peak in summer season and mainly caused by influenza A H3N2 and 3 – 6 years children were more susceptible to the influenza-like infection during 2021 – 22 in Xiamen city.

ABSTRACT The global COVID-19 pandemic and frequent outbreaks of other infectious diseases, such as influenza and monkeypox, often initially manifest with non-specific influenza-like symptoms. Portability to the outbreak site and accurate identification of the various pathogens causing influenza-like illness are crucial for the rapid implementation of effective control measures. Utilizing real-time PCR and microfluidic technology, a multiplexed on-site sample-in-result-out test through microfluidic real-time PCR (MONITOR) has been developed for detecting the pathogens responsible for influenza-like illness. The operator simply needs to introduce the sample to a highly integrated microfluidic chip (requiring ≤1 minute), and the system can autonomously execute sample pre-processing, nucleic acid extraction, and PCR amplification and deliver results for eight pathogens within 85 minutes. The detection limit of MONITOR for the eight pathogens ranges from 0.78 to 6.25 copies/µL. Standard curves demonstrate notable linearity and amplification efficiency. A simulated clinical sample test reveals MONITOR’s sensitivity, specificity, and accuracy at 97.5%, 100%, and 98%, respectively. Bland-Altman analysis demonstrates strong agreement between the cycle threshold of positive MONITOR samples and quantitative polymerase chain reaction (qPCR) (R 2 = 0.952), suggesting MONITOR’s ability to indirectly quantify pathogen load. The fully enclosed structure of the system chip minimizes sample and environmental cross-contamination, rendering the assay independent of a specialized laboratory. The portable, fully automated MONITOR facilitates on-site, comprehensive detection of multiple pathogens, even non-specialized operators with minimal training. This presents a promising approach for the rapid and precise diagnosis of influenza-like illness in grassroots communities and underserved rural areas lacking centralized laboratories. IMPORTANCE This study combines quantitative polymerase chain reaction (qPCR) and microfluidics to introduce MONITOR, a portable field detection system for multiple pathogens causing influenza-like illness. MONITOR can be rapidly deployed to enable simultaneous sample-in-result-out detection of eight common influenza-like illness (ILI) pathogens with heightened sensitivity and specificity. It is particularly well suited for communities and regions without centralized laboratories, offering robust technical support for the prompt and accurate monitoring and detection of ILI. It holds the potential to be a potent tool in the early detection and prevention of infectious diseases.

Influenza-like illness (ILI) remains a major cause of severe mortality and morbidity in the elderly. Aging is associated with a decreased ability to sense pathogens and mount effective innate and adaptive immune responses, thus mandating the development of protective nutraceuticals. Biobran/MGN-3, an arabinoxylan from rice bran, has potent anti-aging and immunomodulatory effects, suggesting that it may be effective against ILI. The objective of the current study was to investigate the effect of Biobran/MGN-3 on ILI incidence, natural killer (NK) cell activity, and the expressions of RIG-1 (retinoic acid-inducible gene 1), MDA5 (melanoma differentiation-associated protein 5), and their downstream signaling genes ISG-15 (interferon-stimulated genes 15) and MX1 (myxovirus (influenza) resistance 1, interferon-inducible). A double-blind, placebo-controlled clinical trial included eighty healthy older adults over 55 years old, 40 males and 40 females, who received either a placebo or Biobran/MGN-3 (500 mg/day) for 3 months during known ILI seasonality (peak incidence) in Egypt. The incidence of ILI was confirmed clinically according to the WHO case definition criteria. Hematological, hepatic, and renal parameters were assessed in all subjects, while the activity of NK and NKT (natural killer T) cells was assessed in six randomly chosen subjects in each group by the degranulation assay. The effect of Biobran/MGN-3 on RIG-1 and MDA5, as well as downstream ISG15 and MX1, was assessed in BEAS-2B pulmonary epithelial cells using flow cytometry. The incidence rate and incidence density of ILI in the Biobran/MGN-3 group were 5.0% and 0.57 cases per 1000 person-days, respectively, compared to 22.5% and 2.95 cases per 1000 person-days in the placebo group. Furthermore, Biobran/MGN-3 ingestion significantly enhanced NK activity compared to the basal levels and to the placebo group. In addition, Biobran/MGN-3 significantly upregulated the expression levels of RIG-1, MDA5, ISG15, and MX1 in the human pulmonary epithelial BEAS-2B cell lines. No side effects were observed. Taken together, Biobran/MGN-3 supplementation enhanced the innate immune response of elderly subjects by upregulating the NK activity associated with reduction of ILI incidence. It also upregulated the intracellular RIG-1, MDA5, ISG15, and MX1 expression in pulmonary epithelial tissue cultures. Biobran/MGN-3 could be a novel agent with prophylactic effects against a wide spectrum of respiratory viral infections that warrants further investigation.

Aim: To examine the clinical significance of thrombocytosis (platelets > 500 × 109/L) in admitted children with an influenza‐like illness. Methods: We performed a database analysis consisting of patients evaluated at our medical centers with an influenza‐like illness between 2009 and 2013. We included paediatric patients and examined the association between platelet count, respiratory viral infections, and admission outcomes (hospital length of stay and admission to the paediatric intensive care unit) using regression models adjusting for multiple variables. Results: A total of 5171 children were included in the study cohort (median age 0.8 years; interquartile range, 0.2–1.8; 58% male). Younger age, and not the type of viral infection, was associated with a high platelet count (p < 0.001). Elevated platelet count independently predicted admission outcomes (p ≤ 0.05). The presence of thrombocytosis was associated with an increased risk for a prolonged length of stay (odds ratio = 1.2; 95% Confidence interval = 1.1 to 1.4; p = 0.003) and admission to the paediatric intensive care unit (odds ratio = 1.5; 95% Confidence interval = 1.1 to 2.0; p = 0.002). Conclusion: In children admitted with an influenza‐like illness, a high platelet count is an independent predictor of admission outcomes. Platelet count may be used to improve risk assessment and management decisions in these paediatric patients. [ABSTRACT FROM AUTHOR]

Background: Influenza surveillance aims to determine onset, duration and intensity of the seasonal Influence-like Illness (ILI); data collection begins in the week 42 of a year and ends in the week 17 of the following year. In this observational study, we report the experience of a tertiary care children hospital in Rome about Influenza viruses circulation during the calendar year 2022 (January-December) in comparison with the previous five years (2017–2021), with a special focus on the weeks 18–41, usually not under surveillance. Methods: This retrospective study involved 36782 respiratory samples referred to 21354 patients (pts), median age 2.63 years, admitted with respiratory symptoms at Bambino Gesù Children's Hospital in the years 2017–2022. Respiratory viruses were detected by molecular Allplex™ Respiratory Panel Assays (Seegene, Korea). Results: Regarding the pre pandemic years, 2017–2019, distribution of Flu positive patients focused in the first weeks of the year (weeks 1–17). During the pandemic period, Flu was not detected. In 2022, 239 Flu viruses were identified: 37 FluA (weeks 1–17), 29 FluA (weeks 18–41) and 168 FluA and 5 FluB (weeks 42–52). For the year 2022, during the non-epidemic period, the number of Flu viruses detected corresponded to 12.1% of total Flu detected, respect to 0-1.7% for the previous five years (p < 0.001). Conclusions: When compared with pre SARS-CoV-2 pandemic years, our data show a significant increase in Influenza cases during weeks 18–41/2022 and reveal an unexpected summer circulation of these viruses: just weeks 26–30 showed to be influenza virus free. A national year-round Flu surveillance could be useful to understand if changing in influenza epidemiology is transitional or likely to persist in the following years. [ABSTRACT FROM AUTHOR]

Objectives: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record–based surveillance algorithm for COVID-19–like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases. Materials and Methods: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases. Results: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020–January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses. Practice Implications: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, many patients presented to the emergency department (ED) with features of Influenza-like illnesses (ILI) and with other atypical presentations. This study was done to determine the etiology, co-infections, and clinical profile of patients with ILI. Methods: This prospective observational study included all patients presenting to the ED with fever and/or cough, breathing difficulty, sore throat, myalgia, gastrointestinal complaints (abdominal pain/vomiting/diarrhea), loss of taste and altered sensorium or asymptomatic patients who resided in or travelled from containment zones, or those who had contact with COVID-19 positive patients during the first wave of the pandemic between April and August 2020. Respiratory virus screening was done on a subset of COVID-19 patients to determine co-infection. Results: During the study period, we recruited 1462 patients with ILI and 857 patients with the non-ILI presentation of confirmed COVID-19 infection. The mean age group of our patient population was 51.4 (SD: 14.9) years with a male predominance (n-1593; 68.7%). The average duration of symptoms was 4.1 (SD: 2.9) days. A sub-analysis to determine an alternate viral etiology was done in 293 (16.4%) ILI patients, where 54 (19.4%) patients had COVID 19 and co-infection with other viruses, of which Adenovirus (n-39; 14.0%) was the most common. The most common symptoms in the ILI-COVID-19 positive group (other than fever and/or cough and/or breathing difficulty) were loss of taste (n-385; 26.3%) and diarrhea (n- 123; 8.4%). Respiratory rate (27.5 (SD: 8.1)/minute: p-value < 0.001) and oxygen saturation (92.1% (SD: 11.2) on room air; p-value < 0.001) in the ILI group were statistically significant. Age more than 60 years (adjusted odds ratio (OR): 4.826 (3.348-6.956); p-value: