Influenza-like Illness Surveillance Research Articles

Assessing the impact of SARS-CoV-2 on influenza-like illness surveillance trends in the community during the 2023/2024 winter in England

ObjectivesInfluenza-like-illness (ILI) is a commonly used symptom categorization in seasonal disease surveillance focusing on influenza in community and clinical settings. However, SARS-CoV-2 often causes presentation with a similar symptom profile. We explore how SARS-CoV-2-positive individuals can influence surveillance trends for the World Health Organization, the United States Centre for Disease Control, and the European Centre for Disease Control (ECDC) ILI criteria. MethodsHarnessing the Winter COVID-19 Infection Study in England, a cohort study, the prevalence of different ILI definitions is modeled using multilevel regression and poststratification using age and spatial stratifications with temporal smoothing. Trends over time across stratifications were compared for SARS-CoV-2 positive and negative individuals to understand differences in ILI trends. Symptom presentation across positive and negative SARS-CoV-2 cases were compared. ResultsSARS-CoV-2 symptom profiles are shown to overlap with the ILI case definitions, particularly for “cough” and “fever”, causing SARS-CoV-2 positive individuals to be frequently detected as ILI cases. The trend of SARS-CoV-2 positives is a substantial component of the ILI-modeled trend, driving an earlier perceived peak in prevalence. The ECDC symptom definition was most influenced by SARS-CoV-2 positive individuals. ConclusionsUsing a large community cohort we show how SARS-CoV-2 can impact ILI surveillance trends. SARS-CoV-2 makes up a substantial part of the community ILI burden and public health messaging should reflect this when discussing ILI. We show ILI is no longer a strong proxy for influenza activity alone.

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.

Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal

Acute respiratory infection is one of the leading causes of morbidity and mortality among children in low- and middle-income countries. Due to limited diagnostic capability, many respiratory pathogens causing influenza-like illness go undetected. This study aims to detect enterovirus, respiratory syncytial virus, seasonal coronavirus and respiratory pathogens other than influenza in patients with influenza-like illness. A total of 997 (54.3%) respiratory samples (collected in the years 2016–2018) were randomly selected from 1835 influenza-negative samples. The xTAG Respiratory Viral Panel (RVP) FAST v2 panel was used to detect respiratory pathogens including enterovirus/rhinovirus (EV/RV), respiratory syncytial virus (RSV) and seasonal coronavirus (HKU1, OC43, NL63 and 229E). A total of 78.7% (785/997) were positive for respiratory viruses. Of these viruses, EV/RV was detected in 36.3% (362/997), which is the highest number, followed by RSV in 13.7% (137/997). The seasonal coronaviruses HKU1 and OC43 (1.5%, 15/997), NL63 (1.2%, 12/997) and 229E (1%, 10/997) were also detected. The EV/RV-positive samples were sequenced, of which 16.7% (5/30) were confirmed as EVs and were identified as coxsackievirus (CV) types CVB5, CVB3, CV21 and CVB2. The findings of this study highlight the importance of strengthening influenza-like illness surveillance programs in the region by including other respiratory viruses in their scope besides seasonal human influenza viruses.

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

The syndromic surveillance system, exemplified by the influenza-like illness (ILI) surveillance system, has long been crucial in providing early warnings of influenza epidemics. 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. 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.

Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study

Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes. In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation. We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021. By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems. Rockefeller Foundation.

Influenza-like illness surveillance may underestimate the incidence of respiratory syncytial virus in adult outpatients

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. 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. 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. 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.

Seasonality and Genotype Diversity of Human Rhinoviruses during an Eight-Year Period in Slovenia.

Due to the high socioeconomic burden of rhinoviruses, the development of prevention and treatment strategies is of high importance. Understanding the epidemiological and clinical features of rhinoviruses is essential in order to address these issues. Our study aimed to define the seasonality and molecular epidemiology of rhinoviruses in Slovenia. Over a period of eight years, a total of 20,425 patients from sentinel primary healthcare settings and sentinel hospitals were examined for a panel of respiratory viruses in the national programme for the surveillance of influenza-like illnesses and acute respiratory infections. The patients were from all age groups and had respiratory infections of various severity. Infection with a rhinovirus was confirmed using an RT-rPCR in 1834 patients, and 1480 rhinoviruses were genotyped. The molecular analysis was linked to demographical and meteorological data. We confirmed the year-round circulation of rhinoviruses with clear seasonal cycles, resulting in two seasonal waves with peaks in spring and autumn. High levels of genotype variability and co-circulation were confirmed between and within seasons and were analysed in terms of patient age, the patient source reflecting disease severity, and meteorological factors. Our study provides missing scientific information on the genotype diversity of rhinoviruses in Slovenia. As most previous investigations focused on exclusive segments of the population, such as children or hospitalised patients, and for shorter study periods, our study, with its design, size and length, contributes complementary aspects and new evidence-based knowledge to the regional and global understanding of rhinovirus seasonality and molecular epidemiology.

Impact of the COVID-19 related border restrictions on influenza and other common respiratory viral infections in New Zealand.

New Zealand's (NZ) complete absence of community transmission of influenza and respiratory syncytial virus (RSV) after May 2020, likely due to COVID-19 elimination measures, provided a rare opportunity to assess the impact of border restrictions on common respiratory viral infections over the ensuing 2 years. We collected the data from multiple surveillance systems, including hospital-based severe acute respiratory infection surveillance, SHIVERS-II, -III and -IV community cohorts for acute respiratory infection (ARI) surveillance, HealthStat sentinel general practice (GP) based influenza-like illness surveillance and SHIVERS-V sentinel GP-based ARI surveillance, SHIVERS-V traveller ARI surveillance and laboratory-based surveillance. We described the data on influenza, RSV and other respiratory viral infections in NZ before, during and after various stages of the COVID related border restrictions. We observed that border closure to most people, and mandatory government-managed isolation and quarantine on arrival for those allowed to enter, appeared to be effective in keeping influenza and RSV infections out of the NZ community. Border restrictions did not affect community transmission of other respiratory viruses such as rhinovirus and parainfluenza virus type-1. Partial border relaxations through quarantine-free travel with Australia and other countries were quickly followed by importation of RSV in 2021 and influenza in 2022. Our findings inform future pandemic preparedness and strategies to model and manage the impact of influenza and other respiratory viral threats.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

Prediction of Influenza-Like Illness Incidence in Cheras, Malaysia based on Environmental Data using the Generalised Additive Model

Influenza-like illness (ILI) accounts for 500,000 fatalities worldwide yearly. Environmental factors contribute to spread of respiratory infections. This study describes the epidemiological trends of ILI in Cheras, Kuala Lumpur, Malaysia and seeks scientific evidence on ILI activity and environmental factors. Cheras Health District collected ILI surveillance data and the nearest weather station which provided environmental data on mean temperature, humidity, wind speed, cumulative rainfall, particulate matter (PM)10 and PM2.5 levels. A total of 51,245 ILI cases were reported from 1st December 2021 to 30th April 2023. The non-linear relationship between ILI incidence and environmental variables was modelled using the Generalised Additive Model (GAM). The lag time for mean temperature, mean humidity, cumulative rainfall, mean wind speed, PM 10 level, and PM 2.5 level was 2, 3, 3, 3, 0, and 3 days, respectively. The model with the optimal lag better describes ILI case variance (R2=0.5, explained deviance=58%) than the model without lag selection (R2=0.5, explained deviance=57.2%). The Lag Model indicates a significant p-value for PM10 but no significant concurvity between predictor variables. Thus, the final model (R2=0.5, explained deviance=59.2%) has k=15. Higher rainfall, relative humidity, colder temperature, and decreased wind speed increased ILI incidence. PM2.5 and PM10 also contributes to ILI.

Influenza activity and regional mortality for non-small cell lung cancer

Lung cancer is the leading cause of cancer deaths in the United States and worldwide. While influenza illness is known to be particularly dangerous for frail and elderly patients, the relationship between influenza illness and outcomes in patients with cancer remains largely unknown. The Surveillance, Epidemiology, and End Results (SEER) database was queried to identify patients with non-small cell lung cancer (NSCLC) diagnosed between 2009 and 2015. Influenza-like illness (ILI) activity, provided by the Outpatient Influenza-like Illness Surveillance Network of the Center of Disease for Control and Prevention, was merged with the SEER dataset on the state-month level. Regional monthly mortality rates were compared during low versus high flu months in this ecological cohort study. 202,485 patients with NSCLC from 13 SEER-reporting states were included in the analysis. 53 of 1049 state-months (5.1%) had high flu activity. Monthly mortality rates during low and high flu months were 0.041 (95% CI 0.041–0.042) and 0.051 (95% CI 0.050–0.053), respectively (RR 1.24 [95% CI 1.21–1.27]). The association between ILI activity and mortality was observed at the individual state level and in all clinical and regional subgroups. Increased regional influenza activity is associated with higher mortality rates for patients with NSCLC. Vaccine-directed initiatives and increased awareness amongst providers will be necessary to address the growing but potentially preventable burden of influenza-related lung cancer deaths in the U.S.

Persistent predominance of the Victoria lineage of influenza B virus during COVID-19 epidemic in Nanchang, China.

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.

Epidemiology of Influenza-like Illness and Respiratory Viral Etiology in Adult Patients in Taiyuan City, Shanxi Province, China between 2018 and 2019.

To determine the epidemiological status of influenza and understand the distribution of common respiratory viruses in adult patients with influenza-like illness (ILI) cases in Taiyuan City, Shanxi Province, China, epidemiological data between 2018 and 2019 were retrieved from the China Influenza Surveillance Information System, and two sentinel ILI surveillance hospitals were selected for sample collection. All specimens were screened for influenza virus (IFV) and the other 14 common respiratory viruses using real-time polymerase chain reaction. The results of the 2-year ILI surveillance showed that 26,205 (1.37%) of the 1,907,869 outpatients and emergency patients presented with ILI, with an average annual incidence of 297.75 per 100,000 individuals, and ILI cases were predominant in children <15 years (21,348 patients, 81.47%). Of the 2713 specimens collected from adult patients with ILI, the overall detection rate of respiratory viruses was 20.13%, with IFV being the most frequently detected (11.79%) and at a relatively lower rate than other respiratory viruses. Further subtype analysis indicated an alternating or mixed prevalence of H1N1 (2009), H3N2, Victoria, and Yamagata subtypes. This study provides a baseline epidemiological characterization of ILI and highlights the need for a nationwide detection and surveillance system for multiple respiratory pathogens.

Pan-India influenza-like illness (ILI) and Severe acute respiratory infection (SARI) surveillance: epidemiological, clinical and genomic analysis.

Over time, COVID-19 testing has significantly declined across the world. However, it is critical to monitor the virus through surveillance. In late 2020, WHO released interim guidance advising the use of the existing Global Influenza Surveillance and Response System (GISRS) for the integrated surveillance of influenza and SARS-CoV-2. In July 2021, we initiated a pan-India integrated surveillance for influenza and SARS-CoV-2 through the geographically representative network of Virus Research and Diagnostic Laboratories (VRDLs) across 26 hospital and laboratory sites and 70 community sites. A total of 34,260 cases of influenza-like illness (ILI) and Severe acute respiratory infection (SARI) were enrolled from 4 July 2021 to 31 October 2022. Influenza A(H3) and B/Victoria dominated during 2021 monsoon season while A(H1N1)pdm09 dominated during 2022 monsoon season. The SARS-CoV-2 "variants of concern" (VoC) Delta and Omicron predominated in 2021 and 2022, respectively. Increased proportion of SARI was seen in extremes of age: 90% cases in < 1 year; 68% in 1 to 5 years and 61% in ≥ 8 years age group. Approximately 40.7% of enrolled cases only partially fulfilled WHO ILI and SARI case definitions. Influenza- and SARS-CoV-2-infected comorbid patients had higher risks of hospitalization, ICU admission, and oxygen requirement. The results depicted the varying strains and transmission dynamics of influenza and SARS-CoV-2 viruses over time, thus emphasizing the need to continue and expand surveillance across countries for improved decision making. The study also describes important information related to clinical outcomes of ILI and SARI patients and highlights the need to review existing WHO ILI and SARI case definitions.

Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 – July 2021

BackgroundWhile laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data.MethodsA nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman’s and Pearson’s correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data.ResultsOver course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 – 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 – 54.4) and females (45.1, 95% CI: 39.8 – 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 – 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman’s correlation coefficient = 0.49; P < 0.001) (Pearson’s correlation coefficient = 0.67; P < 0.0001).ConclusionsThe ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

Diversity of Non-Influenza Respiratory Viruses Associated with Influenza-Like Illness during 2009 pre and pandemic periods in Sao Paulo, Brazil, a Historical Overview

Background and objective: During the 2009 influenza A (H1N1) pandemic, 60% of the respiratory specimens collected from patients under surveillance for influenza-like illness (ILI) in São Paulo, Brazil, tested negative for influenza A and B by Real-Time RT-PCR (RT-qPCR) assays. In this retrospective study, we identified the diversity of other respiratory viruses associated with ILI during the pandemic period and added the benefit of RT-qPCR for their detection. Methods: Five-hundred and forty-two influenza RT-qPCR negative respiratory specimens from ILI patients collected from Jan - Dec 2009, that also tested negative by a commercial Indirect Immuno Fluorescence (IIF) assay for the Respiratory Syncytial Virus (RSV), Parainfluenza (PIV) PIV1, PIV2, PIV 3 and Adenovirus (AdV) were retested by a panel of RT-qPCR assays for the other respiratory viruses, including RSV, Human Metapneumovirus (HMPV), PIV 1-4, AdV, Human Coronaviruses (HCoV) OC43, 229E, NL63 and HKU1, Rhinovirus (RV), Enterovirus (EV) and Human Bocavirus (HBoV). Results: Of the 542 negative specimens, 428 (78%) were positive by RT-qPCR for another respiratory virus: In descending order: RV (149, 27%); RSV (41, 8%); AdV (32, 6%); HMPV (29, 5%); PIV3 (19, 4%); PIV1 (3, 1%); HCoV-OC43 (9, 2%); HBoV (9, 2%); PIV4 (6, 1%); HCoV-HKU1 (6, 1%); EV (5, 1%); HCoV-229E 4 (4, 1%); HCoV-NL63 (3, 1%). Co-detections of two viruses (98, 18%) and three viruses (12, 2%) were common. Conclusion: This retrospective study revealed a high prevalence of respiratory viruses other than influenza associated with ILI during the pandemic period and highlights the superiority of RT-qPCR over IIF for ILI surveillance.

Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China

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.

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

Wastewater-based epidemiology revealed in advance the increase of enterovirus circulation during the Covid-19 pandemic

Wastewater-based epidemiology (WBE) was conducted to track Enteroviruses (EVs) circulation in the Milan metropolitan area (Northern Italy) during Covid-19 pandemic (March 2020–December 2022).202 composite 24-hour wastewater samples (WWSs) were collected weekly from March 24, 2020, to December 29, 2022 at the inlet of two wastewater treatment plants (WWTP) in Milan (1.5 million inhabitants). EV-RNA was quantified and molecular characterization of non-polio EVs (NPEV) was performed by Sanger sequence analysis. Data from WWS were matched with virological data collected in the framework of Influenza-Like Illness (ILI) surveillance in the same place and time.EV-RNA was identified in 88.2 % of WWSs. The peak in EVs circulation was observed in late August 2020 (upon conclusion of the first national lockdown), in late August 2021, and in mid-April 2022.EV-RNA concentration in WWS (normalized as copies/d/1000 people) at peak of circulation presented a yearly increase (2020: 2.47 × 1010; 2021: 6.81 × 1010; 2022: 2.14 × 1011). This trend overlapped with trend in EV-positivity rate in ILI cases, expanded from 21.7 % in 2021 to 55.6 % in 2022. EV trends in WWS preceded clinical sample detections in 2021 and 2022 by eight and five weeks, respectively, acting as an early warning of outbreak.Although sequencing of EV-positive WWSs revealed the presence of multiple EV strains, typing remained inconclusive. Molecular characterization of EVs in clinical samples revealed the co-circulation of several genotypes: EV-A accounted for 60 % of EVs, EV-B for 16.7 %, EV-D68 for 23.3 %.EVs were circulating in Milan metropolitan area between March 2020 and December 2022. The epidemiological trends unfolded the progressive accumulation of EV transmission in the population after removal of Covid-19 restrictions. The increased circulation of EVs in 2021–2022 was identified at least 35 days in advance compared to the analysis of clinical data. The inconclusive results of Sanger sequencing lookout for improvement and innovative molecular approaches to deepen track EVs.

Preschool-located influenza vaccination and influenza-like illness surveillance: an Italian pilot experience

BackgroundWe describe the first school-located influenza vaccination campaign with quadrivalent live-attenuated influenza vaccine (LAIV) among pre-school children in Italy, coupled with an innovative school-centred influenza-like illnesses (ILIs) surveillance using a self-sampling non-invasive saliva collection method.MethodsThe pilot study was proposed during the 2021/2022 influenza season to fifteen pre-schools in the Milan municipality. LAIV was offered directly in school to all healthy children without contraindications. ILI differential diagnosis was conducted by real-time RT-PCR for influenza A/B and SARS-CoV-2.ResultsFive pre-schools were involved in the pilot project and overall, 135 families (31.2%) participated in the study, adhering to both surveillance and vaccination; 59% of families had an immigrant background. No pupil experienced adverse reactions after vaccination. Nineteen saliva samples were collected from sixteen children (11.8%). Six samples (31.6%) tested positive for SARS-CoV-2; none was positive for influenza A/B.ConclusionsThe participation in the immunisation campaign was good, considering possible absences due to COVID-19 pandemic, and the intranasal administration was well tolerated and helped to overcome parental hesitancy. Saliva sampling represented a useful tool to reduce children’s stress and increase parents’ compliance. The high participation of families with an immigrant background suggests that school-based interventions can represent an effective strategy to overcome socioeconomic and cultural barriers.