Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results.

Background: Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). Objective: This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. Methods: LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. Results: The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. Conclusions: LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.

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 (F2) 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.

Enhanced laboratory surveillance of respiratory infection disclosed the rapid rise of enterovirus D68 cases, northern Italy, August to September 2024.

We report a considerable increase in enterovirus D68 (EV-D68) cases since July 2024, culminating in an ongoing outbreak of acute respiratory infections in northern Italy, accounting for nearly 90% of all enterovirus infections. The outbreak was identified by community- and hospital-based surveillance systems, detecting EV-D68 in individuals with mild-to-severe respiratory infections. These strains belonged to B3 and a divergent A2 lineage. An increase in adult cases was observed. Enhanced surveillance and molecular characterisation of EV-D68 across Europe are needed.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms.

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.

Comparison of Three Influenza Surveillance Data Sources for Timely Detection of Epidemic Onset - Chengdu City, Sichuan Province and Beijing Municipality, China, 2017-2023.

What is already known about this topic?: The syndromic surveillance system, exemplified by the influenza-like illness (ILI) surveillance system, has long been crucial in providing early warnings of influenza epidemics. What is added by this report?: The analysis revealed that employing reported influenza case data from the nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS) enhanced the early detection of influenza epidemics, particularly within the context of multiple respiratory pathogens circulating concurrently. What are the implications for public health practice?: The NIDRIS, characterized by its extensive coverage, obligatory reporting, high specificity, and real-time data transmission, offers a valuable tool for the effective early detection of influenza epidemics. Utilizing this system could enhance preparedness and responses to such health crises, potentially mitigating their impact on public health.

Evaluation of the influenza-like illness case definition and the acute respiratory infection case definition in the diagnosis of influenza and COVID-19 in healthcare personnel.

BACKGROUND: To evaluate the influenza-like illness (ILI) and acute respiratory infection (ARI) case definitions in the diagnosis of COVID-19 and influenza in healthcare personnel (HCP). METHODS: We followed a cohort of 5752 HCP from November 2022 to May 2023. Symptomatic HCP were tested for SARS-CoV-2 and influenza by real-time PCR and/or rapid antigen detection test. ILI was defined as the sudden onset of ≥1 systemic symptom and ≥1 respiratory symptom. ARI was defined as the sudden onset of ≥1 respiratory symptom. Patients with respiratory symptoms were grouped either as ILI or as ARI based on the presence of fever, malaise, headache and/or myalgia. RESULTS: Overall, 466 ILI cases and 383 ARI cases occurred. HCP with ILI had an adjusted odds ratio (aOR) of 22.05 [95% confidence interval (CI): 6.23-78.04] to be diagnosed with influenza. HCP with ARI had an aOR of 2.70 (95% CI: 1.88-3.88) to be diagnosed with COVID-19. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ILI for influenza were 96.6%, 49.9%, 18.2%, and 99.2%, respectively. The sensitivity, specificity, PPV, and NPV of ARI for COVID-19 were 51.7%, 73.6%, 84.9%, and 34.8%, respectively. ILI and ARI had an overall correct classification rate of 89.6% and 74.1%, respectively. CONCLUSION: Our findings support the use of both ILI and ARI case definitions in the diagnosis of influenza and COVID-19 in HCP.

Caregiver Burden among Patients with Influenza or Influenza-like Illness (ILI): A Systematic Literature Review.

Influenza and influenza-like illness (ILI) pose significant clinical and economic burdens globally each year. This systematic literature review examined quantitative studies evaluating the impact of patients' influenza/ILI on their caregivers' well-being, focusing on health-related quality of life (HRQoL), work productivity, and activity impairment. A comprehensive search across six databases, including the Cochrane Database of Systematic Reviews, Embase, MEDLINE via PubMed, Ovid, PsycNet, and Web of Science, yielded 18,689 records, of which 13,156 abstracts were screened, and 662 full-text articles were reviewed from January 2007 to April 2024. Thirty-six studies [HRQoL: 2; productivity: 33; both: 1] covering 22 countries were included. Caregivers of 47,758 influenza or ILI patients across 123 study cohorts were assessed in the review. The mean workday loss among caregivers ranged from 0.5 to 10.7 days per episode, influenced by patients' influenza status (positive or negative), disease severity (mild or moderate-to-severe), age, viral type (influenza A or B), and vaccination/treatment usage. The HRQoL of caregivers, including their physical and emotional well-being, was affected by a patient's influenza or ILI, where the severity and duration of a patient's illness were associated with worse HRQoL. This review shows that the consequences of influenza or ILI significantly affect not only patients but also their caregivers.

Real-world effectiveness of influenza vaccine against medical-attended influenza infection during 2023/24 season in Ili Kazakh Autonomous Prefecture, China: A test-negative, case-control study.

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.

Prospective cohort study of patient demographics, viral agents, seasonality, and outcomes of influenza-like illness in Mexico in the late H1N1-pandemic and post-pandemic years (2010-2014).

Objectives: Influenza-like illness (ILI) caused by respiratory viruses results in various respiratory clinical manifestations. The ILI002 prospective observational cohort study aimed to describe viral agents, seasonality, and outcomes of patients with ILI during four seasons in the influenza H1N1-pandemic and post-pandemic years (2010-2014). Methods: Patients from six Mexican hospitals were enrolled from April 2010 to March 2014. Clinical data and nasopharyngeal swabs were obtained and tested for viral respiratory pathogens by real-time reverse-transcription polymerase chain reaction. Results: Of the 5662 enrolled participants, 64.9% were adults and 35.1% were children. Among the 5629 participants with single-pathogen detection, rhinovirus (20.2%), influenza virus (11.2%), respiratory syncytial virus (RSV) (7.2%), and coronavirus (6.8%) were the most frequent pathogens. Co-infection occurred in 14.5% of cases; 49.3% of participants required hospitalization, particularly in RSV cases (42.9% adults, 89.6% children). The mortality rate was 2.8% higher among older adult participants and those with comorbidities. Influenza H1N1 had the highest mortality rate, yet almost half of the deceased had no pathogen. Rhinovirus persisted year-round, while influenza, coronavirus, and RSV peaked during cooler months. Conclusions: Analyses showed that some viruses causing ILI may lead to severe disease and hospitalization irrespective of comorbidities. These findings may help in decision-making about public health policies on prevention measures, vaccination, treatment, and administration of health care.

Laboratory capacity expansion: lessons from establishing molecular testing in regional referral laboratories in Ethiopia.

Respiratory viruses contribute to high morbidity and mortality in Africa. In 2020, the Ohio State University's Global One Health Initiative, in collaboration with the Ethiopian Public Health Institute and the US Centers for Disease Control and Prevention, took action to strengthen Ethiopia's existing respiratory virus surveillance system through decentralization of laboratory testing and scale-up of national and regional capacity for detecting respiratory viruses. In August 2022, four regional laboratories were established, thereby raising the number of reference laboratories conducting respiratory virus surveillance to five. This article highlights lessons learned during implementation and outlines processes undertaken for laboratory scale-up and decentralization.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Enterovirus D68 disease burden and epidemiology in hospital-admitted influenza-like illness, Valencia region of Spain, 2014-2020 influenza seasons.

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.

Outbreak of influenza and SARS-CoV-2 at the Armed Forces of the Philippines Health Service Education and Training Center, September 25-October 10, 2023.

In the last week of September 2023, a surge of influenza-like illness was observed among students of the Armed Forces of the Philippines (AFP) Health Service Education and Training Center, where 48 (27 males and 21 females; age in years: mean 33, range 27-41) of 247 military students at the Center presented with respiratory symptoms. Between September 25 and October 10, 2023, all 48 symptomatic students were evaluated with real-time reverse transcription polymerase chain reaction and sequencing for both influenza and SARS-CoV-2. Thirteen (27%) students were found positive for influenza A/H3 only, 6 (13%) for SARS-CoV-2 only, and 4 (8%) were co-infected with influenza A/H3 and SARS-CoV-2. Seventeen influenza A/ H3N2 viruses belonged to the same clade, 3C.2a1b.2a.2a.3a, and 4 SARSCoV-2 sequences belonged to the JE1.1 lineage, indicating a common source outbreak for both. The influenza A/H3N2 circulating virus belonged to a different clade than the vaccine strain for 2023 (3C.2a1b.2a.2a). Only 4 students had received the influenza vaccine for 2023. In response, the AFP Surgeon General issued a memorandum to all military health institutions on October 19, 2023 that mandated influenza vaccination as a prerequisite for enrollment of students at all education and training centers, along with implementation of non-pharmaceutical interventions and early notification and testing of students exhibiting influenza-like-illness.

Clinical profile of children with influenza like illness during pre-monsoon at coastal Karaikal, Puducherry, India.

Influenza infections in developing countries are under reported and WHO estimates that nearly 99% of influenza deaths worldwide occur in children under-five years of age in Asian and African countries. Consequently, this study aims to analyze the use of clinical profile and easily available laboratory parameters to aid identification of the possible viral etiology in the setting of pre-monsoon ILI. A cross-sectional study was carried out for three months among children with ILI attending fever clinic of a tertiary care hospital in Karaikal, South India. In the study population the prevalence of ILI was highest in the age group four to five years followed by school aged children. Adolescents were affected the least. Influenza B was most common virus causing ILI in this region, followed by covid-19 infection. Laboratory parameters depicted a significantly high ESR in COVID-19 infected ILI children. They also exhibited leucopenia and normal platelet counts. Clinical symptoms and laboratory parameters which are easily available and cheaper can be used in resource poor settings of healthcare to identify possible influenza and COVID-19 infected children amongst cases presenting with ILI.

Effectiveness of cocoon strategy vaccination on prevention of influenza-like illness in young infants.

During 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.

Effect of COVID-19 pandemic on influenza; observation of a tertiary level virology laboratory.

The lockdown enforced amid the COVID-19 pandemic has affected the occurrence and trends of various respiratory virus infections, with a particular focus on influenza. Our study seeks to analyze the repercussions of the COVID-19 pandemic on the positivity of the influenza virus throughout a 4-year span, encompassing both the pre-COVID-19 era (2018 and 2019) and the COVID-19 period (2020 and 2021). Data collected from patients clinically diagnosed with Influenza-like Illness and Severe Acute Respiratory Illness (SARI) from January 2018 to December 2021 for influenza virus detection were acquired and analyzed through multiplex RT-qPCR. The statistical analysis was conducted using SPSS (Statistical Package for Social Sciences) Version 21.0 Software. A total of 4464 samples were tested over 4 years (2018-2021), with 3201 samples from the pre-COVID era and 1263 samples from the COVID era. Influenza A positivity dropped from 17.7 to 9.57% and Influenza B positivity decreased from 3.74 to 2.61%. Subtyping revealed changes in prevalence for both viruses. Seasonal variations showed more pronounced peaks in the pre-COVID-19 era with reduced activity during lockdown. Influenza A saw a resurgence in August 2021. Throughout the COVID-19 pandemic (2020-2021) SARI cases did not decrease. The positivity rate for Influenza A slightly rose to 7.79% from 4.23% in the COVID period (2020-2021). This increase correlates with heightened hospitalization rates during the pandemic, sparking concerns of potential coinfection with coronavirus and Influenza A. The notable drop in influenza cases in 2020-2021 is likely due to stringent precautions, lockdowns, drug repurposing, and prioritized testing, indicating no reduction in influenza transmission. Increased influenza positivity in SARI patients during COVID-19 highlights a heightened risk of coinfection. Emphasizing solely on COVID-19 may lead to underreporting of other respiratory pathogens, including influenza viruses.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Air Pollution and Influenza: A Systematic Review and Meta-Analysis.

Background: Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases. Methods: We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1). Results: A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively. Conclusion: Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.

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.

Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study.

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.