Influenza-like Illness Incidence Rate Research Articles

Cost-Effectiveness of Extending the National Influenza Vaccination Program in South Korea: Does Vaccination of Older Adults Provide Health Benefits to the Entire Population?

The South Korean government has successfully improved influenza vaccination coverage for individuals aged 65 years or older as part of its National Immunization Program (NIP). Those aged 50–64 years without funded vaccination care have significantly lower vaccination rates and face a substantial risk of influenza-related complications. We use a dynamic epidemiological and economic model to investigate the cost-effectiveness of expanding the universal vaccine fund to include those aged 50–64. The epidemiological model is estimated using the susceptibility-infection-recovery model and influenza and influenza-like illness incidence rates, which were calculated by the National Health Insurance Service–National Sample Cohort from the 2008/09 to 2012/13 influenza seasons but excluding the 2009/10 season for pandemic influenza A (H1N1). The decision tree economic model is assessed from societal and healthcare sector perspectives. The proposed policy would eliminate 340,000 annual influenza cases and prevent 119 unnecessary deaths. From a societal perspective, the proposed policy would reduce costs by USD 68 million. From a healthcare perspective, the cost is USD 4318 per quality-adjusted life years. Within the study range, sensitivity analyses found consistent cost-effectiveness results. The influenza vaccine for adults aged 50–64 appears to be cost-saving or cost-effective and, thus, should be considered for the NIP.

Dynamics of influenza-like illness under urbanization procedure and COVID-19 pandemic in the subcenter of Beijing during 2013-2021.

Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.

Influenza epidemiology and burden of disease in Mongolia, 2013–2014 to 2017–2018

Background: Mongolia is a vast, sparsely populated country in central Asia. Its harsh climate and nomadic lifestyle make the population vulnerable to acute respiratory infections, particularly influenza. Evidence on the morbidity, mortality and socioeconomic impact of influenza in Mongolia is scarce; however, routine surveillance for influenza-like illness (ILI), severe acute respiratory infection (SARI) and laboratory-detected influenza is conducted. This paper describes the epidemiology of influenza and the estimated burden of influenza-associated illness in Mongolia in the five influenza seasons between 2013–2014 and 2017–2018. Methods: Demographic and laboratory data from 152 sentinel surveillance sites on all patients who met the case definitions of ILI and SARI between October 2013 and May 2018 were extracted and analysed as described in A Manual for Estimating Disease Burden Associated with Seasonal Influenza. Results: The estimated annual influenza-associated ILI and SARI rates, presented as ranges, were 1279–2798 and 81–666 cases per 100 000 population, respectively. Children aged <5 years accounted for 67% of all ILI cases and 79% of all SARI cases. The annual specimen positivity for influenza was highest (11–30% for ILI and 8–31% for SARI) for children aged 5–<15 years and children <2 years old, respectively. The annual mortality rate due to pneumonia and SARI was highest among children aged <2 years (15.8–54.0 per 100 000 population). Although the incidence of influenza-associated ILI and SARI was lowest for people aged >65 years, the mortality rate due to pneumonia and SARI (1.2–5.1 per 100 000) was higher than that for those aged 15–64 years. Conclusion: The estimated influenza-associated ILI and SARI incidence rates are high in Mongolia, and children, especially those aged <5 years, have the highest influenza-associated burden in Mongolia. These findings provide evidence for decision-makers in Mongolia to consider targeted influenza vaccination, particularly for children.

Influenza epidemiology and burden of disease in Mongolia, 2013-2014 to 2017-2018.

BackgroundMongolia is a vast, sparsely populated country in central Asia. Its harsh climate and nomadic lifestyle make the population vulnerable to acute respiratory infections, particularly influenza. Evidence on the morbidity, mortality and socioeconomic impact of influenza in Mongolia is scarce; however, routine surveillance for influenza-like illness (ILI), severe acute respiratory infection (SARI) and laboratory-detected influenza is conducted. This paper describes the epidemiology of influenza and the estimated burden of influenza-associated illness in Mongolia in the five influenza seasons between 2013–2014 and 2017–2018.MethodsDemographic and laboratory data from 152 sentinel surveillance sites on all patients who met the case definitions of ILI and SARI between October 2013 and May 2018 were extracted and analysed as described in A Manual for Estimating Disease Burden Associated with Seasonal Influenza.ResultsThe estimated annual influenza-associated ILI and SARI rates, presented as ranges, were 1279–2798 and 81–666 cases per 100 000 population, respectively. Children aged < 5 years accounted for 67% of all ILI cases and 79% of all SARI cases. The annual specimen positivity for influenza was highest (11–30% for ILI and 8–31% for SARI) for children aged 5– < 15 years and children < 2 years old, respectively. The annual mortality rate due to pneumonia and SARI was highest among children aged < 2 years (15.8–54.0 per 100 000 population). Although the incidence of influenza-associated ILI and SARI was lowest for people aged 365 years, the mortality rate due to pneumonia and SARI (1.2–5.1 per 100 000) was higher than that for those aged 15–64 years.ConclusionThe estimated influenza-associated ILI and SARI incidence rates are high in Mongolia, and children, especially those aged < 5 years, have the highest influenza-associated burden in Mongolia. These findings provide evidence for decision-makers in Mongolia to consider targeted influenza vaccination, particularly for children.

A comparative study between the incidence and epidemiological features of Influenza-Like Illness and laboratory-confirmed COVID-19 cases in the Italian epicenter (Lombardy)

IntroductionIn Lombardy, the influenza surveillance system relies on sentinel physicians that weekly report data on the number of Influenza-Like Illness (ILI) and a part of them also collect nasopharyngeal samples for virologic analyses. This study aims at comparing the ILI incidence of 2019–2020 influenza season with the incidence of COVID-19 cases in order to better understand the current epidemic and to evaluate whether the implementation of ILI surveillance system could succeed in early detection and monitoring of COVID-19 diffusion. MethodsThe distribution of ILI cases in the seasons 2017–2018, 2018–2019 and 2019–2020 was taken in consideration and the curve trends were compared and analyzed according to geographical areas, age groups and time differences. ResultsThe curve trends presented a similar pattern up to the 9th week; in fact, a reduction in the ILI incidence rate was observed in the 2017–2018 and 2018–2019 season but in the 2019–2020 an increase in the reported ILI emerged. The relation between the numbers reported by 2019–2020 ILI surveillance and those reported for COVID-19 is supported by the curve trends, the correspondence between age groups, the correspondence by geographical location, and also by the results of the nasopharyngeal swab tests performed. DiscussionThe influenza surveillance system is an effective tool for early detection of COVID-19. It may provide timely and high-quality data evaluating the SARS-CoV-2 burden among population with ILI. Implementation of the system has to be prioritized in order to identify any future novel respiratory pathogen with pandemic potential.

Influenza surveillance system and Covid-19

The clinical presentation of symptomatic SARS-CoV-2 not only includes viral pneumonia, but also milder illness overlapping with influenza-like illness (ILI), allowing a potential tracking of the infection in the framework of the national influenza surveillance system (SS). By comparing the data recorded by the influenza SS in 2019-20 season to those collected for the previous years, we want to evaluate whether the implementation of ILI SS could succeed in early detection and monitoring of Covid-19 diffusion.We analyzed the data recorded by the influenza SS and we compared the distribution of ILI incidence rate by week for 2017-18, 2018-19 and 2019-20 season in order to understand whether the SS detected any abnormality coinciding with Covid-19 outbreak.The distribution of ILI cases in the three seasons presented a similar pattern up to the 9th week; after then, a reduction in the ILI incidence rate was observed in the 2017-18 and the 2018-19 season while an increase was detected for 2019-20. During 2019-20 season, three major characteristics stand out: i) at the beginning of Covid-19 epidemic (7th-9th week) 9,17/1000 cases were reported; ii) during the recognition of the COVID-19 outbreak (9th-10th week) 6,36/1000 cases; iii) during the spread of Covid-19 (10th-11th week) an unexpected increase to 7,72/1000 cases. Additionally, their geographical distribution was concentrated in the areas known to be most affected by the epidemic.The influenza SS enabled us to detect the introduction and distribution of COVID-19. Implementation of the system should be prioritized in order to early identify new waves of Covid-19 but also any future novel respiratory pathogen. In order to empower the SS, it would be advisable to increase the population coverage about 2% which is the actual standard.Key messages The influenza surveillance system detected the first wave of Covid-19 in Lombardy Region, Italy.The influenza surveillance system should be implemented in order to bring a benefit both to the current situation and in sight of future public health challenges.

A spatio-temporal analysis of influenza-like illness in Iran from 2011 to 2016.

Background: Investigating the spatial aspects of the disease can help decision-makers and researchers better understand the pattern of the disease, and is also very important in the implementation of the disease control programs. Given the vast area of Iran, as well as the diverse geographical and climate conditions of the country, using the geographical information system (GIS) is a suitable method for the study of influenza. In this study, we provide a clear picture of the distribution of the influenza-like illness (ILI) in Iranian provinces through the years from 2011 to 2016 by applying a spatio-temporal analysis, using geographic information system (GIS). Disease rates by location and year are estimated, and then hot spots and cold spots are distinguished. Methods: This study was conducted using the ILI incidence rate data recorded in the Iranian Influenza Surveillance System from August 2011 to August 2016. The Choropleth map method and the various equal interval and natural break classifications were used. The local Getis-Ord Gi* method was then used to identify the hot spots and regions where, for some reason, the distribution of the disease had significantly clustered spatially. Statistical analyses were done using the ArcGIS 10.2 software. Results: This study indicates that the highest ILI rate belongs to the period from August 2014 to August 2015 with a rate of 180.26 (95%CI: 177.65 to 182.9) per 100,000 people. The results show that the highest 5-year mean of ILI rate belongs to Zanjan, Markazi, Lorestan, Ilam, North Khorasan, and South Khorasan provinces. Also, results from the local Getis-Ord Gi* method show that ILI has formed a hot spot between the years 2011 and 2013 on the eastern borders of Iran and afterward during the years 2014 to 2016 in the western regions of the country. Conclusion: Given the importance of influenza and its huge economic burden on the society, identifying the hot spot regions can help better manage the disease. This study indicates the distribution of the disease has formed a hot spot in the western regions of the country.

A population-based study on healthcare-seeking behaviour of persons with symptoms of respiratory and gastrointestinal-related infections in Hong Kong

BackgroundStudies on healthcare-seeking behaviour usually adopted a patient care perspective, or restricted to specific disease conditions. However, pre-diagnosis symptoms may be more relevant to healthcare-seeking behaviour from a patient perspective. We described healthcare-seeking behaviours by specific symptoms related to respiratory and gastrointestinal-related infections.MethodsWe conducted a longitudinal population-based telephone survey in Hong Kong. We collected data on healthcare-seeking behaviour specific to symptoms of respiratory and gastrointestinal-related infections and also associated demographic factors. We performed descriptive analyses and estimated the proportion of participants who sought medical consultation, types of services utilized and duration from symptom onset to healthcare seeking, by different age groups. Post-stratification was used to compensate non-response and multiple imputation to handle missing and right-censored data.ResultsWe recruited 2564 participants who reported a total of 4370 illness episodes and 7914 symptoms. Fatigue was the most frequently reported symptom, followed by headache and runny nose, with 30-day incidence rate of 9.1, 7.7, and 7.7% respectively. 78% of the participants who had fever sought medical consultation, followed by those with rash (60%) and shortness of breath (58%). Older adults (aged ≥55y) who had symptoms including fever, sore throat, and headache had a significantly higher consultation rate comparing to the other age groups. The 30-day incidence rates of influenza-like illness (ILI) and acute respiratory illness (ARI) were 0.8 and 7.2% respectively, and the consultation rates among these participants were 91 and 64%. Private general practitioner clinics was the main service utilized by participants for most of the symptoms considered, especially those related to acute illness such as fever, diarrhoea and vomiting. Chinese medicine clinics were mostly likely to be visited by participants with low back pain, myalgia and fatigue. Among participants who have sought medical services, most were within 3 days of symptom onset.ConclusionsHealthcare-seeking behaviour were different by symptoms and age. Characterization of these patterns provides crucial parameters for estimating the full burden of common infectious diseases from facility-based surveillance system, for planning and allocation of healthcare resources.

Burden and seasonality of medically attended influenza like illness (ILI) in Ethiopia, 2012 to 2017

BackgroundThe influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. Influenza-related incidence data are limited in many countries in Africa despite established sentinel surveillance. This study aimed to address the information gap by estimating the burden and seasonality of medically attended influenza like illness in Ethiopia.MethodInfluenza sentinel surveillance data collected from 3 influenza like illness (ILI) and 5 Severe Acute Respiratory Illness (SARI) sites from 2012 to 2017 was used for analysis. Descriptive statistics were applied for simple analysis. The proportion of medically attended influenza positive cases and incidence rate of ILI was determined using total admitted patients and catchment area population. Seasonality was estimated based on weekly trend of ILI and predicted threshold was done by applying the “Moving Epidemic Method (MEM)”.ResultA total of 5715 medically attended influenza suspected patients who fulfills ILI and SARI case definition (77% ILI and 23% SARI) was enrolled. Laboratory confirmed influenza virus (influenza positive case) among ILI and SARI suspected case was 25% (1130/4426) and 3% (36/1289). Of which, 65% were influenza type A. The predominantly circulating influenza subtype were seasonal influenza A(H3N2) (n = 455, 60%) and Influenza A(H1N1)pdm09 (n = 293, 38.81%). The estimated mean annual influenza positive case proportion and ILI incidence rate was 160.04 and 52.48 per 100,000 population. The Incidence rate of ILI was higher in the age group of 15–44 years of age [‘Incidence rate (R) = 254.6 per 100,000 population’, 95% CI; 173.65, 335.55] and 5–14 years of age [R = 49.5, CI 95%; 31.47, 130.43]. The seasonality of influenza has two peak seasons; in a period from October–December and from April–June.ConclusionSignificant morbidity of influenza like illness was observed with two peak seasons of the year and seasonal influenza A (H3N2) remains the predominantly circulating influenza subtype. Further study need to be considered to identify potential risks and improving the surveillance system to continue early detection and monitoring of circulating influenza virus in the country has paramount importance.

Using the MCRISP Network to Study Acute Gastroenteritis and Influenza-Like Illness Outbreaks in Child Care Centers Compared to Statewide Epidemics

Introduction:Biosurveillance is critical for early detection of disease outbreaks and resource mobilization. Child care center (CCC) attendance has long been recognized as a significant independent predictor for respiratory and gastrointestinal diseases, but CCC surveillance is currently not part of the statewide disease surveillance system. The Michigan Child Care Related Infections Surveillance Program (MCRISP) is an independent, online reporting network with &gt;30 local CCCs that was created to fill this surveillance gap.Aim:To describe the capability of a novel CCC biosurveillance system (MCRISP) to report pediatric Influenza-Like Illness (ILI) and Acute Gastroenteritis (AGE) illness over three years to (i) assess both the timing and magnitude of epidemics in CCCs and (ii) compare CCC outbreak patterns with those of the state database.Methods:MCRISP collates real-time syndromic reports of illness from local county CCCs. The statewide Michigan Disease Surveillance System (MDSS) collects reports of diagnosed illness from designated laboratories, clinics, and hospitals statewide. We assessed epidemic curves based on MCRISP incidence rates and MDSS case counts for ILI and AGE over three seasons (2014-7).Results:A total of 4,627 MCRISP cases (2,425 ILI and 2,202 AGE reports) were reported during the three years of study surveillance. Epidemic patterns (seasonal peaks, troughs, and breadth) for both ILI and AGE in CCCs mirrored those reported at county and state levels, respectively. Two distinguishing features of CCC ILI outbreaks were noted in all three seasons: MCRISP ILI rates remained elevated after MDSS influenza counts abated, and MCRISP rates consistently peaked prior to MDSS influenza peaks. Neither of these phenomena were observed in comparing AGE outbreaks between surveillance systems.Discussion:ILI and AGE incidence rates from the MCRISP network appeared to broadly mirror epidemics from the established state surveillance system. MCRISP may act as a sentinel system for larger community outbreaks of respiratory disease.

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study.

BackgroundTraditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users’ activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data.ObjectiveHere, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time.MethodsWe used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models—random forest, elastic net, and support vector machine (SVM).ResultsFor national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model.ConclusionsWe found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable.

Climatic factors and long-term trends of influenza-like illness rates in The Netherlands, 1970–2016

BackgroundClimatic factors affect the survival and transmissibility of respiratory viruses causing influenza-like illness (ILI), and we hypothesized that changes in absolute humidity and temperature may affect long-term trends of ILI incidence rate in temperate countries. We tested this hypothesis using ILI and meteorological time series in the Netherlands for the period 1970–2016. MethodsWe described the long-term trends of ILI incidence, absolute humidity and temperature; modelled the association between climatic factors and ILI activity using negative binomial regression models; and assessed the strength of the association between the seasonal average absolute humidity (or temperature) and ILI incidence rate using the Spearman's rank correlation coefficient. ResultsThe ILI incidence rate declined from 1970 and reached a minimum in the season 2002–03, but started to increase again from the season 2003–04 onwards. In the negative binominal regression models, the weekly ILI count was inversely associated (p < 0.001) with 0- and 1-week lagged absolute humidity and temperature. After three decades of rising absolute humidity and temperature (1970–2000), the early 2000s represented a trend-reversal point for the climatic time series. The seasonal average ILI incidence rate and absolute humidity (or temperature) were strongly (inversely) correlated. ConclusionsOur findings suggest that climate change may have played a role in the long-term trends of ILI incidence rates in the Netherlands, as we were able to show that lower humidity and temperature in a given week were associated with higher ILI incidence in the next week, there was a clear time point reversal in climatic parameters and ILI rates in the 2000s, and the average annual ILI incidence was inversely related to average annual temperatures and humidity.

Linking temperature and humidity to influenza-like illness: A long-term time series study in the Netherlands, 1970–2016

Introduction Climatic parameters affect the survival and transmissibility of respiratory viruses causing influenza-like illness (ILI), like influenza, respiratory syncytial virus and rhinovirus. In temperate countries, ILI activity is inversely associated with humidity and temperature (which affect both the onset and size of ILI epidemics), and we hypothesized that changes in the average values of these climatic parameters (which have occurred in Europe in recent decades) may explain the declining trends in ILI incidence rates observed during the same period. Here, we tested this hypothesis by linking ILI and climatic time series in the Netherlands for the period 1970–2016. Methods Influenza-like illness surveillance data for the period 1970–2016 were obtained from the Netherlands Institute for Health Services Research (NIVEL), which relies on a network of 60 sentinel general practitioners in 40 practices that are distributed throughout the Netherlands (including urban and rural areas) and cover approximately 0.7% of the Dutch population. Data on daily average absolute humidity (g/m3) and temperature for the weather station of De Bilt (which is located in the middle of the country) for the period 1970–2016 were obtained from the Royal Netherlands Meteorological Institute. Linear regression models were used to assess whether the season average ILI incidence rate changed over time, and whether there was any change over time in daily average absolute humidity and temperature. We modelled the association between climate and ILI activity through negative binomial regression models with the weekly ILI count as dependent variable, absolute humidity (or temperature) with up to three lagged weeks as independent variables, and covariates accounting for contagiousness of ILI-causing agents and presence of immunity. Model-based predictions were made using a leave-one-out cross-validation procedure. The study was funded by Sanofi-Pasteur. Results The season average ILI incidence rate declined significantly (P Conclusions Changes in absolute humidity and temperature were able to satisfactorily explain the long-term trends of ILI incidence rate in the Netherlands during 1970–2016. Temperate countries should monitor climate-related change in infectious diseases epidemiology so that measures to mitigate their impact can be planned and implemented.

Real-time predictive seasonal influenza model in Catalonia, Spain.

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.

Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe.

BackgroundThe wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries.ObjectiveIn this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system.MethodsInfluenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone.ResultsIn summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time.ConclusionsResults from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases.

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without control measures and compared with the actual influenza outbreak to evaluate the effectiveness of early control efforts. The ILI incidence rates in 2014 (14.51%) was higher than the incidence in 2013 (5.27%) and 2012 (3.59%). Ten school influenza outbreaks were detected by CUSUM. Each outbreak had high transmissibility with a median Runc of 4.62. The interventions in each outbreak had high effectiveness and all Rcon were 0. The early intervention had high effectiveness within the school-based ILI syndromic surveillance. Syndromic surveillance within schools can play an important role in controlling influenza outbreaks.

Cross-protection to new drifted influenza A(H3) viruses and prevalence of protective antibodies to seasonal influenza, during 2014 in Portugal

IntroductionImmune profile for influenza viruses is highly changeable over time. Serological studies can assess the prevalence of influenza, estimate the risk of infection, highlight asymptomatic infection rate and can also provide data on vaccine coverage. The aims of the study were to evaluate pre-existing cross-protection against influenza A(H3) drift viruses and to assess influenza immunity in the Portuguese population. Materials and methodsWe developed a cross-sectional study based on a convenience sample of 626 sera collected during June 2014, covering all age groups, both gender and all administrative health regions of Portugal. Sera antibody titers for seasonal and new A(H3) drift influenza virus were evaluated by hemagglutination inhibition assay (HI). Seroprevalence to each seasonal influenza vaccine strain virus and to the new A(H3) drift circulating strain was estimated by age group, gender and region and compared with seasonal influenza-like illness (ILI) incidence rates before and after the study period. ResultsOur findings suggest that seroprevalences of influenza A(H3) (39.9%; 95% CI: 36.2–43.8) and A(H1)pdm09 (29.7%; 95% CI: 26.3–33.4) antibodies were higher than for influenza B, in line with high ILI incidence rates for A(H3) followed by A(H1)pdm09, during 2013/2014 season. Low pre-existing cross-protection against new A(H3) drift viruses were observed in A(H3) seropositive individuals (46%). Both against influenza A(H1)pdm09 and A(H3) seroprotection was highest in younger than 14-years old. Protective antibodies against influenza B were highest in those older than 65years old, especially for B/Yamagata lineage, 33.3% (95% CI: 25.7–41.9). Women showed a high seroprevalence to influenza, although without statistical significance, when compared to men. A significant decreasing trend in seroprotection from north to south regions of Portugal mainland was observed. ConclusionsOur results emphasize that low seroprotection increases the risk of influenza infection in the following winter season. Seroepidemiological studies can inform policy makers on the need for vaccination and additional preventive measures.

Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015

ObjectiveTo assess the effects of winter/summer school breaks on occurrences of influenza-like illness (ILI).MethodsWe jointly analysed ILI surveillance data with the timing of school breaks in a temperate district in...

Is the onset of influenza in the community age-related?

We studied the spread of influenza in the community between 1993 and 2009 using primary-care surveillance data to investigate if the onset of influenza was age-related. Virus detections [A(H3N2), B, A(H1N1)] and clinical incidence of influenza-like illness (ILI) in 12·3 million person-years in the long-running Royal College of General Practitioners-linked clinical-virological surveillance programme in England & Wales were examined. The number of days between symptom onset and the all-age peak ILI incidence were compared by age group for each influenza type/subtype. We found that virus detection and ILI incidence increase, peak and decrease were in unison. The mean interval between symptom onset to peak ILI incidence in virus detections (all ages) was: A(H3N2) 20·5 [95% confidence interval (CI) 19·7-21·6] days; B, 18·8 (95% CI 15·8·0-21·7) days; and A(H1N1) 17·0 (95% CI 15·6-18·4) days. Differences by age group were examined using the Kruskal-Wallis test. For A(H3N2) and A(H1N1) viruses the interval was similar in each age group. For influenza B there were highly significant differences by age group (P = 0·0001). Clinical incidence rates of ILI reported in the 8 weeks preceding the period of influenza virus activity were used to estimate a baseline incidence and threshold value (upper 95% CI of estimate) which was used as a marker of epidemic progress. Differences between the age groups in the week in which the threshold was reached were small and not localized to any age group. In conclusion we found no evidence to suggest that influenza A(H3N2) and A(H1N1) occurs in the community in one age group before another. For influenza B, virus detection was earlier in children aged 5-14 years than in persons aged ⩾25 years.