Respiratory Illness Surveillance Research Articles

Construction of data source indicator system for acute respiratory infectious disease surveillance based on the Delphi method

Objective: To establish an indicator system for surveillance of data sources to provide a theoretical basis for respiratory infectious disease surveillance and early warning. Methods: Indicators for data sources in the surveillance of acute respiratory infectious diseases were initially compiled through a literature search. Subsequently, two rounds of expert consultations were conducted with 22 experts using the Delphi method to refine the indicators. Results: The questionnaire recovery rates for the two rounds of expert consultation were 100.00% and 86.36%, respectively. The authority coefficient of the experts was 0.83. The coordination coefficient of the second round of Delphi expert consultation was 0.32, and the coefficient of variation of each indicator was less than 0.25. Finally, the indicators system of data source for the surveillance of acute respiratory infectious diseases includes 4 first-level indicators, 10 second-level indicators, and 26 third-level indicators. Conclusion: The indicator system of data sources for the surveillance of acute respiratory infectious diseases constructed in this study is reasonable and reliable, providing a valuable reference for surveillance, early warning and policy formulation of acute respiratory infectious diseases.

Prospective clinical surveillance for severe acute respiratory illness and COVID-19 vaccine effectiveness in Kenyan hospitals during the COVID-19 pandemic.

There are limited data from sub-Saharan Africa describing the demographic characteristics, clinical features and outcome of patients admitted to public hospitals with severe acute respiratory infections during the COVID-19 pandemic. We conducted a prospective longitudinal hospital-based sentinel surveillance between May 2020 and December 2022 at 16 public hospitals in Kenya. All patients aged above 18 years admitted to adult medical wards in the participating hospitals were included. We collected data on demographic and clinical characteristics, SARS-CoV-2 infection and COVID-19 vaccination status and, admission episode outcomes. We determined COVID-19 vaccine effectiveness (VE) against admission with SARS-CoV-2 positive severe acute respiratory illness (SARI) (i.e., COVID-19) andprogression to inpatient mortality among patients admitted with SARI, using a test-negative case control design. Of the 52,636 patients included in the study, 17,950 (34.1%) were admitted with SARI. The median age was 50 years. Patients were equally distributed across sexes. Pneumonia was the most common diagnosis at discharge. Hypertension, Human Immunodeficiency Virus (HIV) infection and Diabetes Mellitus were the most common chronic comorbidities. SARS-CoV-2 test results were positive in 2,364 (27.9%) of the 8,471 patients that underwent testing. After adjusting for age, sex and presence of a chronic comorbidity, SARI patients were more likely to progress to inpatient mortality compared to non-SARI patients regardless of their SARS-CoV-2 infection status (adjusted odds ratio (aOR) for SARI and SARS-CoV-2 negative patients 1.22, 95% CI 1.10-1.37; and aOR for SARI and SARS-CoV-2 positive patients 1.32, 95% CI 1.24-1.40). After adjusting for age, sex and presence of a chronic comorbidity, COVID-19 VE against progression to inpatient mortality following admission with SARI for those with a confirmed vaccination status was 0.59 (95% CI 0.27-0.77). We have provided a comprehensive description of the demographic and clinical pattern of admissions with SARI in Kenyan hospitals during the COVID-19 pandemic period as well as the COVID-19 VE for these patients. These data were useful in providing situational awareness during the first three years of the pandemic in Kenya and informing national response measures.

Respiratory Disease Surveillance in the Middle East and Latin America during the COVID-19 Pandemic, 2020-2022.

Characterizing the epidemiology of circulating respiratory pathogens during the COVID-19 pandemic could clarify the burden of acute respiratory infections and monitor outbreaks of public health and military relevance. The US Department of Defense supported 2 regions for influenza-like illness and severe acute respiratory infections surveillance, one in the Middle East through US Naval Medical Research Unit EURAFCENT, and another in Latin America through US Naval Medical Research Unit SOUTH. During 2020‒2022, coinciding with the COVID-19 pandemic, we collected a total of 16,146 nasopharyngeal and oropharyngeal swab samples from sentinel sites in Jordan (n = 11,305) and Latin America (n = 4,841). Samples were tested for SARS-CoV-2, influenza, and other respiratory pathogens. SARS-CoV-2 was the most frequently detected pathogen during 2020; other respiratory pathogens had distinct temporal and frequency distributions according to geographic location. Our findings support the need for continued sentinel surveillance as a vital tool for assessing the burden of respiratory diseases globally.

Clinical and Genomic Epidemiology of Coxsackievirus A21 and Enterovirus D68 in Homeless Shelters, King County, Washington, USA, 2019-2021.

Congregate homeless shelters are disproportionately affected by infectious disease outbreaks. We describe enterovirus epidemiology across 23 adult and family shelters in King County, Washington, USA, during October 2019-May 2021, by using repeated cross-sectional respiratory illness and environmental surveillance and viral genome sequencing. Among 3,281 participants >3 months of age, we identified coxsackievirus A21 (CVA21) in 39 adult residents (3.0% [95% CI 1.9%-4.8%] detection) across 7 shelters during October 2019-February 2020. We identified enterovirus D68 (EV-D68) in 5 adult residents in 2 shelters during October-November 2019. Of 812 environmental samples, 1 was EV-D68-positive and 5 were CVA21-positive. Other enteroviruses detected among residents, but not in environmental samples, included coxsackievirus A6/A4 in 3 children. No enteroviruses were detected during April 2020-May 2021. Phylogenetically clustered CVA21 and EV-D68 cases occurred in some shelters. Some shelters also hosted multiple CVA21 lineages.

Near real-time severe acute respiratory illness surveillance characterising influenza and COVID-19 epidemiology in hospitalised adults, 2021-22

We report the findings of a novel enhanced syndromic surveillance thatcharacterised influenza- and SARS-CoV-2-associated severe acute respiratory illness (SARI) in the 2021/2022 winter season. Prospective cohort study of adults admitted to the Queen Elizabeth University Hospital, Glasgow, with a severe acute respiratory illness. Patient demographics, clinical history, admission details, and outcomes were recorded. Data were available to Public Health Scotland (PHS) and clinicians weekly. Between November 2021 and May 2022, 1063 hospitalised SARI episodes in 1037 adult patients were identified. Median age was 72.0 years, and 44.5% were male. Most (82.6%) SARI cases had ≥1 co-morbidity; chronic lung disease (50.0%) and malignancy (22.5%) were the most frequently reported. Overall, 229 (22%) and 33 (3%) SARI episodes were SARS-CoV-2 and influenza A PCR positive, respectively. 74.7%, 6.5% and 43.0% SARI episodes received antibiotics, antivirals, and steroids, respectively (54.5%, 11.0% and 51.3% among COVID-19 patients). 1.1% required mechanical ventilation and 7.8% died. Male sex, multimorbidity, frailty, respiratory rate >30, low GCS and chest X-ray consolidation were predictive of in-hospital mortality. Near real-time hospitalised SARI syndromic surveillance characterised the evolving clinical epidemiology of SARS-CoV-2 and influenza, high antimicrobial use, and predictors of inpatient mortality among hospitalised SARI patients.

Updated systematic review of respiratory health surveillance: what is the evidence and what are the knowledge gaps?

Occupational health surveillance of respiratory disease, if done properly, should contribute to reducing the burden of occupational lung diseases. However, this systematic review shows that the quality and extent of evidence on the effectiveness of occupational health surveillance for respiratory disease is very limited. Therefore, it is essential to expand the evidence base in order to make informed decisions on the need for any changes to established guidance on health surveillance.

Influenza C virus in U.S. children with acute respiratory infection 2016–2019

Influenza C virus (ICV) is an orthomyxovirus related to influenza A and B, yet due to few commercial assays, epidemiologic studies may underestimate incidence of ICV infection and disease. We describe the epidemiology and characteristics of ICV within the New Vaccine Surveillance Network (NVSN), a Centers for Disease Control and Prevention (CDC)-led network that conducts population-based surveillance for pediatric acute respiratory illness (ARI). Nasal or/combined throat swabs were collected from emergency department (ED) or inpatient ARI cases, or healthy controls, between 12/05/2016–10/31/2019 and tested by molecular assays for ICV and other respiratory viruses. Parent surveys and chart review were used to analyze demographic and clinical characteristics of ICV+ children. Among 19,321 children tested for ICV, 115/17,668 (0.7 %) ARI cases and 8/1653 (0.5 %) healthy controls tested ICV+. Median age of ICV+ patients was 18 months and 88 (71.5 %) were ≤36 months. Among ICV+ ARI patients, 40 % (46/115) were enrolled in the ED, 60 % (69/115) were inpatients, with 15 admitted to intensive care. Most ICV+ ARI patients had fever (67.8 %), cough (94.8 %), or wheezing (60.9 %). Most (60.9 %) ARI cases had ≥1 co-detected viruses including rhinovirus, RSV, and adenovirus. In summary, ICV detection was rarely associated with ARI in children, and most ICV+ patients were ≤3 years old with co-detected respiratory viruses.

Respiratory Syncytial Virus-Associated Hospitalizations in Children

The coronavirus disease 2019 pandemic disrupted respiratory syncytial virus (RSV) seasonality resulting in early, atypical RSV seasons in 2021 and 2022, with an intense 2022 peak overwhelming many pediatric healthcare facilities. We conducted prospective surveillance for acute respiratory illness during 2016-2022 at 7 pediatric hospitals. We interviewed parents, reviewed medical records, and tested respiratory specimens for RSV and other respiratory viruses. We estimated annual RSV-associated hospitalization rates in children aged <5 years and compared hospitalization rates and characteristics of RSV-positive hospitalized children over 4 prepandemic seasons (2016-2020) to those hospitalized in 2021 or 2022. There was no difference in median age or age distribution between prepandemic and 2021 seasons. Median age of children hospitalized with RSV was higher in 2022 (9.6 months vs 6.0 months, P < .001). RSV-associated hospitalization rates were higher in 2021 and 2022 than the prepandemic average across age groups. Comparing 2021 to 2022, RSV-associated hospitalization rates were similar among children <2 years of age; however, children aged 24 to 59 months had significantly higher rates of RSV-associated hospitalization in 2022 (rate ratio 1.68 [95% confidence interval 1.37-2.00]). More RSV-positive hospitalized children received supplemental oxygen and there were more respiratory virus codetections in 2022 than in prepandemic seasons (P < .001 and P = .003, respectively), but there was no difference in the proportion hypoxemic, mechanically ventilated, or admitted to intensive care. The atypical 2021 and 2022 RSV seasons resulted in higher hospitalization rates with similar disease severity to prepandemic seasons.

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.

Descriptive Epidemiology of Pathogens Associated with Acute Respiratory Infection in a Community-Based Study of K-12 School Children (2015-2023).

School-based outbreaks often precede increased incidence of acute respiratory infections in the greater community. We conducted acute respiratory infection surveillance among children to elucidate commonly detected pathogens in school settings and their unique characteristics and epidemiological patterns. The ORegon CHild Absenteeism due to Respiratory Disease Study (ORCHARDS) is a longitudinal, laboratory-supported, school-based, acute respiratory illness (ARI) surveillance study designed to evaluate the utility of cause-specific student absenteeism monitoring for early detection of increased activity of influenza and other respiratory viruses in schools from kindergarten through 12th grade. Eligible participants with ARIs provided demographic, epidemiologic, and symptom data, along with a nasal swab or oropharyngeal specimen. Multipathogen testing using reverse-transcription polymerase chain reaction (RT-PCR) was performed on all specimens for 18 respiratory viruses and 2 atypical bacterial pathogens (Chlamydia pneumoniae and Mycoplasma pneumoniae). Between 5 January 2015 and 9 June 2023, 3498 children participated. Pathogens were detected in 2455 of 3498 (70%) specimens. Rhinovirus/enteroviruses (36%) and influenza viruses A/B (35%) were most commonly identified in positive specimens. Rhinovirus/enteroviruses and parainfluenza viruses occurred early in the academic year, followed by seasonal coronaviruses, RSV, influenza viruses A/B, and human metapneumovirus. Since its emergence in 2020, SARS-CoV-2 was detected year-round and had a higher median age than the other pathogens. A better understanding of the etiologies, presentations, and patterns of pediatric acute respiratory infections can help inform medical and public health system responses.

Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024.

Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO's mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. We aim to describe the RSC's plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA's sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA's reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC's pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. The RSC extended its surveillance activities to meet more but not all of the mosaic framework's objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.

Multimodal Surveillance Model for Enterovirus D68 Respiratory Disease and Acute Flaccid Myelitis among Children in Colorado, USA, 2022.

Surveillance for emerging pathogens is critical for developing early warning systems to guide preparedness efforts for future outbreaks of associated disease. To better define the epidemiology and burden of associated respiratory disease and acute flaccid myelitis (AFM), as well as to provide actionable data for public health interventions, we developed a multimodal surveillance program in Colorado, USA, for enterovirus D68 (EV-D68). Timely local, state, and national public health outreach was possible because prospective syndromic surveillance for AFM and asthma-like respiratory illness, prospective clinical laboratory surveillance for EV-D68 among children hospitalized with respiratory illness, and retrospective wastewater surveillance led to early detection of the 2022 outbreak of EV-D68 among Colorado children. The lessons learned from developing the individual layers of this multimodal surveillance program and how they complemented and informed the other layers of surveillance for EV-D68 and AFM could be applied to other emerging pathogens and their associated diseases.

Epidemiology of Human Seasonal Coronaviruses Among People With Mild and Severe Acute Respiratory Illness in Blantyre, Malawi, 2011-2017.

The aim of this study was to characterize the epidemiology of human seasonal coronaviruses (HCoVs) in southern Malawi. We tested for HCoVs 229E, OC43, NL63, and HKU1 using real-time polymerase chain reaction (PCR) on upper respiratory specimens from asymptomatic controls and individuals of all ages recruited through severe acute respiratory illness (SARI) surveillance at Queen Elizabeth Central Hospital, Blantyre, and a prospective influenza-like illness (ILI) observational study between 2011 and 2017. We modeled the probability of having a positive PCR for each HCoV using negative binomial models, and calculated pathogen-attributable fractions (PAFs). Overall, 8.8% (539/6107) of specimens were positive for ≥1 HCoV. OC43 was the most frequently detected HCoV (3.1% [191/6107]). NL63 was more frequently detected in ILI patients (adjusted incidence rate ratio [aIRR], 9.60 [95% confidence interval {CI}, 3.25-28.30]), while 229E (aIRR, 8.99 [95% CI, 1.81-44.70]) was more frequent in SARI patients than asymptomatic controls. In adults, 229E and OC43 were associated with SARI (PAF, 86.5% and 89.4%, respectively), while NL63 was associated with ILI (PAF, 85.1%). The prevalence of HCoVs was similar between children with SARI and controls. All HCoVs had bimodal peaks but distinct seasonality. OC43 was the most prevalent HCoV in acute respiratory illness of all ages. Individual HCoVs had distinct seasonality that differed from temperate settings.

Summary of key viral respiratory infectious diseases surveillance in some countries and regions in the world

Respiratory virus infections spread quickly, and some diseases are even more likely to cause a global epidemic. In order to explore how better to carry out surveillance of viral respiratory infectious diseases, the author searched the surveillance situation of key viral respiratory infectious diseases in America, Europe, and China, et al., then put forward several suggestions such as strengthening application of information technology, broaden monitoring channels, building early monitoring and early warning mechanisms.

SARS-CoV-2 co-detection with influenza and human respiratory syncytial virus in Ethiopia: Findings from the severe acute respiratory illness (SARI) and influenza-like illness (ILI) sentinel surveillance, January 01, 2021, to June 30, 2022.

SARS-CoV-2 co-infection with the influenza virus or human respiratory syncytial virus (RSV) may complicate its progress and clinical outcomes. However, data on the co-detection of SARS-CoV-2 with other respiratory viruses are limited in Ethiopia and other parts of Africa to inform evidence-based response and decision-making. We analyzed 4,989 patients' data captured from the national severe acute respiratory illness (SARI) and influenza-like illness (ILI) sentinel surveillance sites over 18 months period from January 01, 2021, to June 30, 2022. Laboratory specimens were collected from the patients and tested for viral respiratory pathogens by real-time, reverse transcription polymerase chain reaction (RT-PCR) at the national influenza center. The median age of the patients was 14 years (IQR: 1-35 years), with a slight preponderance of them being at the age of 15 to less than 50 years. SARS-CoV-2 was detected among 459 (9.2%, 95% CI: 8.4-10.0) patients, and 64 (1.3%, 95% CI: 1.0-1.6) of SARS-CoV-2 were co-detected either with Influenza virus (54.7%) or RSV (32.8%) and 12.5% were detected with both of the viruses. A substantial proportion (54.7%) of SARS-CoV-2 co-detection with other respiratory viruses was identified among patients in the age group from 15 to less than 50 years. The multivariable analysis found that the odds of SARS-CoV-2 co-detection was higher among individuals with the age category of 20 to 39 years as compared to those less than 20 years old (AOR: 1.98, 95%CI:1.15-3.42) while the odds of SARS-CoV-2 co-detection was lower among cases from other regions of the country as compared to those from Addis Ababa (AOR:0.16 95%CI:0.07-0.34). Although the SARS-CoV-2 co-detection with other respiratory viral pathogens was minimal, the findings of this study underscore that it is critical to continuously monitor the co-infections to reduce transmission and improve patient outcomes, particularly among the youth and patients with ILI.

900. The Epidemiology of Viral Coinfections within Households with Children

Abstract Background Improvements in virus detection using molecular assays, such as multiplex polymerase chain reaction, has led to more common identification of viral coinfections. The incidence of viral coinfections may depend on population factors, such as age structure, distribution of chronic respiratory conditions, geographic location, and setting of the study population (e.g., community, in-patient admission), which has new relevance given observations of coinfections involving SARS-CoV-2. Household-level data on mild to moderate illnesses offer an important complement to other coinfection data that are largely measured in clinical settings. Methods The Household Influenza Vaccine Evaluation (HIVE) study is an ongoing prospective cohort study of households with children in Southeast Michigan that began in 2010. Active acute respiratory illness (ARI) surveillance is conducted, and participants with eligible ARI’s are tested for a range of respiratory pathogens. Descriptive statistics were used to characterize viral coinfections within this cohort for a period prior to the COVID-19 pandemic (2010-2020) and will be performed for a period during the pandemic (2020-2022). The consequences of these coinfections in terms of symptoms and household transmission are under evaluation. Results There were 6,245 virus-positive ARI’s in the cohort from 2010-2020, with multiple viruses detected in 896 (14.3%) ARI’s. Of the coinfections, rhinovirus/enterovirus most frequently appeared (58.15%), followed by common CoV (38.84%), and AdV (24%) (See Figure 1). The most common virus combination was a coronavirus (229E, OC43, NL63, or HKU1) along with rhinovirus/enterovirus, making up 12.05% (n=108) of coinfections. Eight infections were made up of four or more viruses. Similar analyses will be conducted for pandemic-period data. Conclusion While awareness of viral coinfections has grown since the start of the COVID-19 pandemic, coinfections within the HIVE cohort were not uncommon prior to the pandemic. Coronaviruses were the second leading viruses identified in coinfections, which aligns with observations noted during the pandemic. These results contribute important information about coinfections and expand the understanding of respiratory virus dynamics within households. Disclosures Emily T. Martin, PhD, MPH, Merck: Grant/Research Support Arnold Monto, MD, Roche: Advisor/Consultant|Roche: Honoraria

RVdb: a comprehensive resource and analysis platform for rhinovirus research.

Rhinovirus (RV), a prominent causative agent of both upper and lower respiratory diseases, ranks among the most prevalent human respiratory viruses. RV infections are associated with various illnesses, including colds, asthma exacerbations, croupand pneumonia, imposing significant and extended societal burdens. Characterized by a high mutation rate and genomic diversity, RV displays a diverse serological landscape, encompassing a total of 174 serotypes identified to date. Understanding RV genetic diversity is crucial for epidemiological surveillance and investigation of respiratory diseases. This study introduces a comprehensive and high-quality RV data resource, designated RVdb (http://rvdb.mgc.ac.cn), covering 26909 currently identified RV strains, along with RV-related sequences, 3D protein structuresand publications. Furthermore, this resource features a suite of web-based utilities optimized for easy browsing and searching, as well as automatic sequence annotation, multiple sequence alignment (MSA), phylogenetic tree construction, RVdb BLAST and a serotyping pipeline. Equipped with a user-friendly interface and integrated online bioinformatics tools, RVdb provides a convenient and powerful platform on which to analyse the genetic characteristics of RVs. Additionally, RVdb also supports the efforts of virologists and epidemiologists to monitor and trace both existing and emerging RV-related infectious conditions in a public health context.

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

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

Surveillance of respiratory viruses at health facilities from across Kenya, 2014

Background: Acute respiratory illnesses (ARI) are a major cause of morbidity and mortality globally. With (re)emergence of novel viruses and increased access to childhood bacterial vaccines, viruses have assumed greater importance in the aetiology of ARI. There are now promising candidate vaccines against some of the most common endemic respiratory viruses. Optimal delivery strategies for these vaccines, and the need for interventions against other respiratory viruses, requires geographically diverse data capturing temporal variations in virus circulation. Methods: We leveraged three health facility-based respiratory illness surveillance platforms operating in 11 sites across Kenya. Nasopharyngeal (NP) and/or oropharyngeal (OP) specimens, patient demographic, and clinical characteristics were collected in 2014 from individuals of various ages presenting with respiratory symptoms at the surveillance facilities. Real time multiplex polymerase chain reaction was used to detect rhinoviruses, respiratory syncytial virus (RSV), influenza virus, human coronaviruses (hCoV), and adenoviruses. Results: From 11 sites, 5451 NP/OP specimens were collected and tested from patients. Of these, 40.2% were positive for at least one of the targeted respiratory viruses. The most frequently detected were rhinoviruses (17.0%) and RSV A/B (10.5%), followed by influenza A (6.2%), adenovirus (6.0%) and hCoV (4.2%). RSV was most prevalent among infants aged &lt;12 months old (18.9%), adenovirus among children aged 12–23 months old (11.0%), influenza A among children aged 24–59 months (9.3%), and rhinovirus across all age groups (range, 12.7–19.0%). The overall percent virus positivity varied by surveillance site, health facility type and case definition used in surveillance. Conclusions: We identify rhinoviruses, RSV, and influenza A as the most prevalent respiratory viruses. Higher RSV positivity in inpatient settings compared to outpatient clinics strengthen the case for RSV vaccination. To inform the design and delivery of public health interventions, long-term surveillance is required to establish regional heterogeneities in respiratory virus circulation and seasonality.

Risk of Subsequent Respiratory Virus Detection After Primary Virus Detection in a Community Household Study-King County, Washington, 2019-2021.

The epidemiology of respiratory viral infections is complex. How infection with one respiratory virus affects risk of subsequent infection with the same or another respiratory virus is not well described. From October 2019 to June 2021, enrolled households completed active surveillance for acute respiratory illness (ARI), and participants with ARI self-collected nasal swab specimens; after April 2020, participants with ARI or laboratory-confirmed severe acute respiratory syndrome coronavirus 2 and their household members self-collected nasal swab specimens. Specimens were tested using multiplex reverse-transcription polymerase chain reaction for respiratory viruses. A Cox regression model with a time-dependent covariate examined risk of subsequent detections following a specific primary viral detection. Rhinovirus was the most frequently detected pathogen in study specimens (406 [9.5%]). Among 51 participants with multiple viral detections, rhinovirus to seasonal coronavirus (8 [14.8%]) was the most common viral detection pairing. Relative to no primary detection, there was a 1.03-2.06-fold increase in risk of subsequent virus detection in the 90 days after primary detection; risk varied by primary virus: human parainfluenza virus, rhinovirus, and respiratory syncytial virus were statistically significant. Primary virus detection was associated with higher risk of subsequent virus detection within the first 90 days after primary detection.