Predicting the short-term success of human influenza virus variants with machine learning.

Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.

Host susceptibility to severe influenza A virus infection.

Most people exposed to a new flu virus do not notice any symptoms. A small minority develops critical illness. Some of this extremely broad variation in susceptibility is explained by the size of the initial inoculum or the influenza exposure history of the individual; some is explained by generic host factors, such as frailty, that decrease resilience following any systemic insult. Some demographic factors (pregnancy, obesity, and advanced age) appear to confer a more specific susceptibility to severe illness following infection with influenza viruses. As with other infectious diseases, a substantial component of susceptibility is determined by host genetics. Several genetic susceptibility variants have now been reported with varying levels of evidence. Susceptible hosts may have impaired intracellular controls of viral replication (e.g. IFITM3, TMPRS22 variants), defective interferon responses (e.g. GLDC, IRF7/9 variants), or defects in cell-mediated immunity with increased baseline levels of systemic inflammation (obesity, pregnancy, advanced age). These mechanisms may explain the prolonged viral replication reported in critically ill patients with influenza: patients with life-threatening disease are, by definition, abnormal hosts. Understanding these molecular mechanisms of susceptibility may in the future enable the design of host-directed therapies to promote resilience.

Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units.

BACKGROUND: While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, few data are available on more severe cases. With retrospective data from intensive care units (ICU) we aim to fill this current knowledge gap. Using multiple parameters proposed by the World Health Organization we estimate the burden of severe acute respiratory infections (SARI) in the ICU and how this varies between influenza epidemics. METHODS: We analyzed weekly ICU admissions in the Netherlands (2007-2016) from the National Intensive Care Evaluation (NICE) quality registry (100% coverage of adult ICUs in 2016; population size 14 million) to calculate SARI incidence, SARI peak levels, ICU SARI mortality, SARI mean Acute Physiology and Chronic Health Evaluation (APACHE) IV score, and the ICU SARI/ILI ratio. These parameters were calculated both yearly and per separate influenza epidemic (defined epidemic weeks). A SARI syndrome was defined as admission diagnosis being any of six pneumonia or pulmonary sepsis codes in the APACHE IV prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. RESULTS: Annually, an average of 13% of medical admissions to adult ICUs were for a SARI but varied widely between weeks (minimum 5% to maximum 25% per week). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU SARI. Between the eight different influenza epidemics under study, the value of each of the severity parameters varied. Per parameter the minimum and maximum of those eight values were as follows: ICU SARI incidence 558-2400 cumulated admissions nationwide, rate 0.40-1.71/10,000 inhabitants; average APACHE score 71-78; ICU SARI mortality 13-20%; ICU SARI/ILI ratio 8-17 cases per 1000 expected medically attended ILI in primary care); peak-incidence 101-188 ICU SARI admissions in highest-incidence week, rate 0.07-0.13/10,000 population). CONCLUSIONS: In the ICU there is great variation between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU SARI surveillance, as proposed by the World Health Organization, thereby would provide insight into the severity of ongoing influenza epidemics, which differ from season to season.

Characterization of Seasonal Influenza Virus Type and Subtypes Isolated from Influenza Like Illness Cases of 2012.

Background Seasonal influenza is one of the increasing public health burdens in Nepal. Objective The objective of this study was to isolate and characterize the influenza virus type and subtypes of Nepal. Method A total of 1536 throat swab specimens were collected from January to December 2012. Total ribonucleic acid was extracted using Qiagen viral nucleic acid extraction kit and polymerase chain reaction assay was performed following the US; CDC Real-time PCR protocol. Ten percent of positive specimens were inoculated onto Madin-Darby Canine Kidney cells. Isolates were characterized by using reference ferret antisera. Result Of the total specimens (n=1536), influenza virus type A was detected in 196 (22%) cases; of which 194 (99%) were influenza A (H1N1) pdm09 and 2 (1 %) were influenza A/H3 subtype. Influenza B was detected in 684 (76.9%) cases. Influenza A (H1N1) pdm09, A/H3 and influenza B virus were antigenically similar to the recommended influenza virus vaccine candidate of the year 2012. Although sporadic cases of influenza were observed throughout the year, peak was observed during July to November. Conclusion Similar to other tropical countries, A (H1N1) pdm09, A/H3 and influenza B viruses were co-circulated in Nepal.

Vaccine for prevention of mild and moderate-to-severe influenza in children.

BACKGROUND: Commonly used trivalent vaccines contain one influenza B virus lineage and may be ineffective against viruses of the other B lineage. We evaluated the efficacy of a candidate inactivated quadrivalent influenza vaccine (QIV) containing both B lineages. METHODS: In this multinational, phase 3, observer-blinded study, we randomly assigned children 3 to 8 years of age, in a 1:1 ratio, to receive the QIV or a hepatitis A vaccine (control). The primary end point was influenza A or B confirmed by real-time polymerase chain reaction (rt-PCR). Secondary end points were rt-PCR-confirmed, moderate-to-severe influenza and rt-PCR-positive, culture-confirmed influenza. The vaccine efficacy and the effect of vaccination on daily activities and utilization of health care resources were assessed in the total vaccinated cohort (2584 children in each group) and the per-protocol cohort (2379 children in the QIV group and 2398 in the control group). RESULTS: In the total vaccinated cohort, 62 children in the QIV group (2.40%) and 148 in the control group (5.73%) had rt-PCR-confirmed influenza, representing a QIV efficacy of 59.3% (95% confidence interval [CI], 45.2 to 69.7), with efficacy against culture-confirmed influenza of 59.1% (97.5% CI, 41.2 to 71.5). For moderate-to-severe rt-PCR-confirmed influenza, the attack rate was 0.62% (16 cases) in the QIV group and 2.36% (61 cases) in the control group, representing a QIV efficacy of 74.2% (97.5% CI, 51.5 to 86.2). In the per-protocol cohort, the QIV efficacy was 55.4% (95% CI, 39.1 to 67.3), and the efficacy against culture-confirmed influenza 55.9% (97.5% CI, 35.4 to 69.9); the efficacy among children with moderate-to-severe influenza was 73.1% (97.5% CI, 47.1 to 86.3). The QIV was associated with reduced risks of a body temperature above 39°C and lower respiratory tract illness, as compared with the control vaccine, in the per-protocol cohort (relative risk, 0.29 [95% CI, 0.16 to 0.56] and 0.20 [95% CI, 0.04 to 0.92], respectively). The QIV was immunogenic against all four strains. Serious adverse events occurred in 36 children in the QIV group (1.4%) and in 24 children in the control group (0.9%). CONCLUSIONS: The QIV was efficacious in preventing influenza in children. (Funded by GlaxoSmithKline Biologicals; ClinicalTrials.gov number, NCT01218308.).

Rates of neutropenia in adults with influenza A or B: a retrospective analysis of hospitalised patients in South East Queensland during 2015.

Neutropenia in adult patients is often attributed to intercurrent viral infections; however, there are limited data describing the frequency or natural history of this phenomenon. We examined all patients presenting to three large hospitals in the Metro South region of South East Queensland with laboratory-confirmed influenza A or B throughout the 2015 influenza season (January-October). Four hundred and thirty-six patients were studied and 15.3% of this cohort were neutropenic (absolute neutrophil count <2.0 × 109 /L) with no identifiable cause other than the influenza. Importantly, the majority of cases were mild, with absolute neutrophil count remaining >1.0 × 109 /L. The incidence of neutropenia was significantly higher in association with influenza B than influenza A (18.3% vs 10.3%). We conclude that mild, transient neutropenia is common among patients with influenza infection and advise that it should not cause alarm or invite specific investigation unless severe or prolonged.

[Influenza and community acquired pneumonia in German primary care]. / Influenza und ambulant erworbene Pneumonie in hausärztlich tätigen Arztpraxen in Deutschland.

Influenza and community-acquired pneumonia (CAP) impose a considerable annual burden on the German primary care system. Yet there is a lack of epidemiological data from the country's outpatient sector on groups at risk as well as on the complications of these diseases.The Robert Koch Institute (RKI) initiated the study to identify population groups at increased risk for influenza or CAP as well as related comorbidities and sequelae. We present the methodology of the study and the descriptive analysis of the patients.ICD-10-based data was collected in 89 primary health care practices between January 2012 and April 2015 using a data extraction tool developed on behalf of the RKI. Case-based anonymized information was recorded for all patients in whom influenza, CAP or other acute respiratory infections (ARI) were diagnosed. For each patient information on all diagnoses including the date were retrospectively and prospectively collected (each for six months) as well as age, sex and influenza vaccination.Data on 156,803 patients with ARI was collected, of them 7909 patients with influenza (within influenza waves) and 8528 patients with CAP diagnoses. Influenza diagnoses showed a strong seasonal pattern and captured annual influenza waves in Germany. Of the influenza cases 1.6 % had a following diagnosis of CAP within 30 days. Age-specific prevalence of chronic diseases such as asthma and diabetes was significantly higher in the study population as compared to the German population.The developed tool delivers in a standardized fashion ICD-10-coded epidemiological data on population-based burden of influenza and CAP in Germany. As the descriptive analysis showed, the collected dataset is a reliable and solid basis for the further investigations of the study questions.

Pediatric hospitalizations associated with 2009 pandemic influenza A (H1N1) in Argentina.

BACKGROUND: While the Northern Hemisphere experiences the effects of the 2009 pandemic influenza A (H1N1) virus, data from the recent influenza season in the Southern Hemisphere can provide important information on the burden of disease in children. METHODS: We conducted a retrospective case series involving children with acute infection of the lower respiratory tract or fever in whom 2009 H1N1 influenza was diagnosed on reverse-transcriptase polymerase-chain-reaction assay and who were admitted to one of six pediatric hospitals serving a catchment area of 1.2 million children. We compared rates of admission and death with those among age-matched children who had been infected with seasonal influenza strains in previous years. RESULTS: Between May and July 2009, a total of 251 children were hospitalized with 2009 H1N1 influenza. Rates of hospitalization were double those for seasonal influenza in 2008. Of the children who were hospitalized, 47 (19%) were admitted to an intensive care unit, 42 (17%) required mechanical ventilation, and 13 (5%) died. The overall rate of death was 1.1 per 100,000 children, as compared with 0.1 per 100,000 children for seasonal influenza in 2007. (No pediatric deaths associated with seasonal influenza were reported in 2008.) Most deaths were caused by refractory hypoxemia in infants under 1 year of age (death rate, 7.6 per 100,000). CONCLUSIONS: Pandemic 2009 H1N1 influenza was associated with pediatric death rates that were 10 times the rates for seasonal influenza in previous years.

Asthma and severity of 2009 novel H1N1 influenza: a population-based case-control study.

BACKGROUND: Asthma has been shown to be associated with an increased risk of the 2009 novel H1N1 influenza (H1N1) infection among children. However, little is known about the role of asthma in severity of H1N1 infection. OBJECTIVE: To determine the association between asthma and other atopic conditions and severity of H1N1 infection. PATIENTS AND METHODS: We conducted a population-based case-control study. Cases were all Olmsted County, MN residents admitted to the hospital within a week of a positive test for H1N1. Controls who had a positive H1N1 but were not admitted to hospital were individually matched to cases with regard to birth day, gender, clinic registration date, diagnostic method, and calendar month of influenza testing. Asthma was ascertained using predetermined criteria. Data were fit to conditional logistic regression models. RESULTS: There were 46 eligible individuals admitted to hospitals with H1N1 infection during the study period. Ninety-seven controls were individually matched to their corresponding cases. Among cases, 23 (50%) were male and 29 (63.0%) were Caucasians. The median age at hospitalization was 20.7 years. Twenty-five (54.4%) cases had asthma before the date of hospitalization, compared to 33 (34.0%) controls (matched OR: 2.31; 95% CI, 1.13-4.73; p = 0.02). This association approached statistical significance after adjusting for all pertinent covariates (adjusted matched OR: 2.55; 95% CI, 0.98-6.64; p = 0.055). CONCLUSION: Asthma may be associated with severe H1N1 infection. In addition to timely influenza vaccination for asthmatics, consideration for prophylactic treatment for unimmunized asthmatics with significant exposure to influenza and immunized asthmatics with early flu-like symptoms should be given.

When pigs could fly: influenza and the elusive nature of diagnosis.

Diagnosis plays an important role in how we understand disease, and how medicine confirms its status in contemporary society. However, diagnoses are far less concrete than their taxonomies suggest. This essay presents influenza as a case study in the elusive nature of the diagnosis, and in its complicated realities. Using the metaphor of boundary transgression, it reveals the fluidity of diagnosis and the paradoxes presented by the naturalization of diseases. In order to contain influenza, medicine commits other paradoxical transgressions of boundaries. Lay self-diagnosis, use of the lay expression "flu," and wide reliance upon the belief in the influenza-like syndrome are used to attempt to cement a concrete notion of influenza.

Influenza neuraminidase: a druggable target for natural products.

The imminent threat of influenza pandemics and repeatedly reported emergence of new drug-resistant influenza virus strains demonstrate the urgent need for developing innovative and effective antiviral agents for prevention and treatment. At present, influenza neuraminidase (NA), a key enzyme in viral replication, spread, and pathogenesis, is considered to be one of the most promising targets for combating influenza. Despite the substantial medical potential of NA inhibitors (NAIs), only three of these drugs are currently on the market (zanamivir, oseltamivir, and peramivir). Moreover, sudden changes in NAI susceptibility revealed the urgent need in the discovery/identification of novel inhibitors. Nature offers an abundance of biosynthesized compounds comprising chemical scaffolds of high diversity, which present an infinite pool of chemical entities for target-oriented drug discovery in the battle against this highly contagious pathogen. This review illuminates the increasing research efforts of the past decade (2000-2011), focusing on the structure, function and druggability of influenza NA, as well as its inhibition by natural products. Following a critical discussion of publications describing some 150 secondary plant metabolites tested for their inhibitory potential against influenza NA, the impact of three different strategies to identify and develop novel NAIs is presented: (i) bioactivity screening of herbal extracts, (ii) exploitation of empirical knowledge, and (iii) computational approaches. This work addresses the latest developments in theoretical and experimental research on properties of NA that are and will be driving anti-influenza drug development now and in the near future.

Improving the estimation of influenza-related mortality over a seasonal baseline.

BACKGROUND: Existing methods for estimation of mortality attributable to influenza are limited by methodological and data uncertainty. We have used proxies for disease incidence of the three influenza cocirculating subtypes (A/H3N2, A/H1N1, and B) that combine data on influenza-like illness consultations and respiratory specimen testing to estimate influenza-associated mortality in the United States between 1997 and 2007. METHODS: Weekly mortality rate for several mortality causes potentially affected by influenza was regressed linearly against subtype-specific influenza incidence proxies, adjusting for temporal trend and seasonal baseline, modeled by periodic cubic splines. RESULTS: Average annual influenza-associated mortality rates per 100,000 individuals were estimated for the following underlying causes of death: for pneumonia and influenza, 1.73 (95% confidence interval = 1.53-1.93); for chronic lower respiratory disease, 1.70 (1.48-1.93); for all respiratory causes, 3.58 (3.04-4.14); for myocardial infarctions, 1.02 (0.85-1.2); for ischemic heart disease, 2.7 (2.23-3.16); for heart disease, 3.82 (3.21-4.4); for cerebrovascular deaths, 0.65 (0.51-0.78); for all circulatory causes, 4.6 (3.79-5.39); for cancer, 0.87 (0.68-1.05); for diabetes, 0.33 (0.26-0.39); for renal disease, 0.19 (0.14-0.24); for Alzheimer disease, 0.41 (0.3-0.52); and for all causes, 11.92 (10.17-13.67). For several underlying causes of death, baseline mortality rates changed after the introduction of the pneumococcal conjugate vaccine. CONCLUSIONS: The proposed methodology establishes a linear relation between influenza incidence proxies and excess mortality, rendering temporally consistent model fits, and allowing for the assessment of related epidemiologic phenomena such as changes in mortality baselines.

[Genetic diversity and molecular evolution of the influenza A viruses in Russia during 2006-2012].

The results of molecular genetic analysis of more than 280 strains of influenza A virus subtypes H1N1 and H3N2 circulating in Russia in 2006-2012 are presented. The genetic changes underlying the evolution of the virus strains and sensitivity to antiviral drugs were analyzed. Significant changes in the genetic structure of influenza A viruses circulating in the Russian Federation and their phylogenetic affiliation are shown to occur within the studied period. The studies identifying codons under the positive selection in silico in the genes encoding surface proteins of the influenza virus were demonstrated to be efficient for the analysis of the antigenic drift and direction of evolutionary variability of the influenza viruses.

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Severe illness from 2009 pandemic influenza A (H1N1)--Utah, 2009-10 influenza season.

Influenza-associated hospitalizations have been a reportable condition in Utah since 2005, and surveillance for influenza hospitalizations has been a valuable tool for identifying and tracking the population impact of serious influenza illness. During the 2009 influenza A (H1N1) pandemic, Utah public health officials used comparisons with hospitalization data from three previous influenza seasons to rapidly assess the impact of 2009 H1N1 and enable public health authorities to target persons at greatest risk for severe illness. This report summarizes the results of that assessment, which determined that 1,327 2009 H1N1 hospitalizations were reported, compared with an average of 435 seasonal influenza hospitalizations during three previous influenza seasons, and 25.5% of 2009 H1N1 hospitalizations resulted in severe illness (intensive-care unit [ICU] admission or death), compared with 14.0% of seasonal influenza hospitalizations. In addition, 2009 H1N1 disproportionately affected racial/ethnic minorities, pregnant women, and residents of Salt Lake County (the state's most densely populated county). During the 4-month "spring wave" of the H1N1 pandemic, a greater percentage of hospitalizations (30.9%) resulted in severe illness than during the 9-month "fall wave" (23.0%). Surveillance for influenza hospitalizations can provide essential data to public health authorities that will help them identify those populations at greatest risk for severe illness.

Severe and critical cases of H1N1 influenza in pregnancy: a Chinese perspective.

CONTEXT: In 2009, an outbreak of A/H1N1 influenza spread worldwide. Following the start of winter in Liaoning province in China, the number of pregnant women infected with influenza increased significantly. Some of them developed respiratory failure and multiple organ failure. AIMS: The aim of this study was to determine the high-risk factors associated with the development of critical illness in the hospitalized pregnant women with A/H1N1 infection. SETTINGS AND DESIGN: This retrospective cohort study was carried out in the tertiary care obstetric department of a large general hospital. MATERIALS AND METHODS: The clinical data of H1N1 pregnant women hospitalized from November 2009 to January 2010 was reviewed. We classified these cases into severe and critical grades according to H1N1 influenza treatment guidelines. We selected maternal age, gestational age, and the time interval between symptom-onset and hospital admission as related factors of critical illness. STATISTICAL ANALYSIS: Logistic regression analyses to determine the relevance and importance of factors significantly associated with critical illness. RESULTS: Eighteen cases of H1N1 influenza pregnant women were admitted. Ten pregnant women were severe cases and eight pregnant women were critical cases. The maternal age (OR=0.979, 95% CI: 0.749~1.279)and the time interval between symptom-onset and hospital admission (OR=1.41, 95% CI: 0.917~2.169) were not found to be risk factors for critical cases. The significant risk factor associated with critical illness is gestational age (OR=53.726, 95% CI: 131.165~2477.918). The risk varied by weeks of gestation, with an odds ratio of 1.034 (95% CI: 0.968-1.106) during the first trimester, 9.667 (95% CI: 0.750-124.59) during the second trimester, and 87 (95% CI: 6.750-1121.39) during the third trimester. CONCLUSIONS: Gestational age is associated with the risk of developing critical infection. The risk increases with increasing weeks of gestation.

Rapid detection of pandemic influenza in the presence of seasonal influenza.

BACKGROUND: Key to the control of pandemic influenza are surveillance systems that raise alarms rapidly and sensitively. In addition, they must minimise false alarms during a normal influenza season. We develop a method that uses historical syndromic influenza data from the existing surveillance system 'SERVIS' (Scottish Enhanced Respiratory Virus Infection Surveillance) for influenza-like illness (ILI) in Scotland. METHODS: We develop an algorithm based on the weekly case ratio (WCR) of reported ILI cases to generate an alarm for pandemic influenza. From the seasonal influenza data from 13 Scottish health boards, we estimate the joint probability distribution of the country-level WCR and the number of health boards showing synchronous increases in reported influenza cases over the previous week. Pandemic cases are sampled with various case reporting rates from simulated pandemic influenza infections and overlaid with seasonal SERVIS data from 2001 to 2007. Using this combined time series we test our method for speed of detection, sensitivity and specificity. Also, the 2008-09 SERVIS ILI cases are used for testing detection performances of the three methods with a real pandemic data. RESULTS: We compare our method, based on our simulation study, to the moving-average Cumulative Sums (Mov-Avg Cusum) and ILI rate threshold methods and find it to be more sensitive and rapid. For 1% case reporting and detection specificity of 95%, our method is 100% sensitive and has median detection time (MDT) of 4 weeks while the Mov-Avg Cusum and ILI rate threshold methods are, respectively, 97% and 100% sensitive with MDT of 5 weeks. At 99% specificity, our method remains 100% sensitive with MDT of 5 weeks. Although the threshold method maintains its sensitivity of 100% with MDT of 5 weeks, sensitivity of Mov-Avg Cusum declines to 92% with increased MDT of 6 weeks. For a two-fold decrease in the case reporting rate (0.5%) and 99% specificity, the WCR and threshold methods, respectively, have MDT of 5 and 6 weeks with both having sensitivity close to 100% while the Mov-Avg Cusum method can only manage sensitivity of 77% with MDT of 6 weeks. However, the WCR and Mov-Avg Cusum methods outperform the ILI threshold method by 1 week in retrospective detection of the 2009 pandemic in Scotland. CONCLUSIONS: While computationally and statistically simple to implement, the WCR algorithm is capable of raising alarms, rapidly and sensitively, for influenza pandemics against a background of seasonal influenza. Although the algorithm was developed using the SERVIS data, it has the capacity to be used at other geographic scales and for different disease systems where buying some early extra time is critical.

[Validation of a syndromic surveillance system of acute respiratory tract diseases in preschools of Schleswig-Holstein (SHARE)]. / Validierung einer syndromischen Surveillance akuter Respirationstrakt-Erkrankungen in Kindergärten und Kindertagesstätten Schleswig-Holsteins (SHARE).

To obtain reliable, regionalized, and timely data for the spread of seasonal influenza in various age groups, which are preferentially affected by the influenza virus, a syndromic surveillance system for acute respiratory tract infections in Schleswig-Holstein (SHARE) was established in preschools and nurseries starting in 2006. The Schleswig-Flensburg district with 12 of 114 preschools and nurseries and 850 of 5,750 supervised children served as a pilot district. The weekly rates of sickness absenteeism correlated most strongly with the onset of seasonal influenza and with population density during the first half of the year. Mean annual sickness absenteeism levels of above 6% occurred more frequently above a population density of 200 inhabitants/km(2) than below this density (relative risk 2.50, 95% confidence interval 1.18-5.32). By analysis of the receiver-operating characteristic curve, the diagnostic performance of the SHARE system as a classifier for seasonal influenza was determined. The sensitivity was 83% and the specificity was 79% when sickness absence rates exceeded 5%. The performance of the SHARE system correlated with the size of the kindergarten. In 2008, 13 of 15 districts of Schleswig-Holstein participated with 157 of 1,684 kindergarten and 10,300 of 113,000 children. The evaluation for 2008 confirmed that the SHARE system is suitable for the surveillance of seasonal influenza at the district and state levels.