Virological Surveillance Data Research Articles

Mid-Term Estimates of Influenza Vaccine Effectiveness against the A(H1N1)pdm09 Prevalent Circulating Subtype in the 2023/24 Season: Data from the Sicilian RespiVirNet Surveillance System.

The current influenza season started in Italy in October 2023, approaching the epidemic peak in late December (52nd week of the year). We aimed to explore the mid-term virologic surveillance data of the 2023/2024 influenza season (from 16 October 2023 to 7 January 2024) in Sicily, the fourth most populous Italian region. A test-negative design was used to estimate the effectiveness of seasonal influenza vaccine (VE) against A(H1N1)pdm09 virus, the predominant subtype in Sicily (96.2% of laboratory-confirmed influenza cases). Overall, 29.2% (n = 359/1230) of oropharyngeal swabs collected from patients with influenza-like illness (ILI) were positive for influenza. Among the laboratory-confirmed influenza cases, 12.5% (n = 45/359) were vaccinated against influenza, with higher prevalence of laboratory-confirmed diagnosis of influenza A among subjects vaccinated with quadrivalent inactivated standard dose (29.4%), live attenuated intranasal (25.1%), and quadrivalent inactivated high-dose (23.8%) influenza vaccines. Comparing influenza vaccination status for the 2023/2024 season among laboratory-confirmed influenza-positive and -negative samples, higher vaccination rates in influenza-negative samples (vs. positive) were observed in all age groups, except for 45-64 years old, regardless of sex and comorbidities. The overall adjusted VE (adj-VE) was 41.4% [95%CI: 10.5-61.6%], whereas the adj-VE was 37.9% [95%CI: -0.7-61.7%] among children 7 months-14 years old and 52.7% [95%CI: -38.0-83.8%] among the elderly (≥65 years old).

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

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

Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India.

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country's capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration.

Reconstructing the impact of COVID-19 on the immunity gap and transmission of respiratory syncytial virus in Lombardy, Italy

Respiratory syncytial virus (RSV) is a leading cause of hospitalisation and mortality in young children globally. The social distancing measures implemented against COVID-19 in Lombardy (Italy) disrupted the typically seasonal RSV circulation during 2019-2021 and caused substantially more hospitalisations during 2021-2022. The primary aim of this study is to quantify the immunity gap-defined as the increased proportion of the population naïve to RSV infection following the relaxation of COVID-19 restrictions in Lombardy, which has been hypothesised to be a potential cause of the increased RSV burden in 2021-2022. We developed a catalytic model to reconstruct changes in the age-dependent susceptibility profile of the Lombardy population throughout the COVID-19 pandemic. The model is calibrated to routinely collected hospitalisation, syndromic, and virological surveillance data and tested for alternative assumptions on age-dependencies in the risk of RSV infection throughout the pandemic. We estimate that the proportion of the Lombardy population naïve to RSV infection increased by 60.8% (95% CrI: 55.2-65.4%) during the COVID-19 pandemic: from 1.4% (95% CrI: 1.3-1.6%) in 2018-2019 to 2.3% (95% CrI: 2.2-2.5%) before the 2021-2022 season, corresponding to an immunity gap of 0.87% (95% CrI: 0.87-0.88%). We found evidence of heterogeneity in RSV transmission by age, suggesting that the COVID-19 restrictions had variable impact on the contact patterns and risk of RSV infection across ages. We estimate a substantial increase in the population-level susceptibility to RSV in Lombardy during 2019-2021, which contributed to an increase in primary RSV infections in 2021-2022. UK Medical Research Council (MRC), UK Foreign, Commonwealth & Development Office (FCDO), EDCTP2 programme, European Union, Wellcome Trust, Royal Society, EU-MUR PNRR INF-ACT.

Global variability of influenza activity and virus subtype circulation from 2011 to 2023

BackgroundAlthough decreased influenza activity has been reported in many countries during the COVID-19 pandemic, it remains unknown how global influenza activity has changed. We described the global variability of influenza...

Transmission Pattern of Measles Virus Circulating in China During 1993-2021: Genotyping Evidence Supports That China Is Approaching Measles Elimination.

To provide useful insights into measles elimination progress in China, measles surveillance data were reviewed, and the transmission patterns of measles viruses circulating in China during 1993-2021 were analyzed. Measles incidence data from the National Notifiable Disease Reporting System of the China Center for Disease Control and Prevention were analyzed. A total of 17 570 strains were obtained from 30 of 31 provinces in mainland China during 1993-2021. The recommended genotyping window was amplified. Genotyping analysis was conducted for comparison with the reference strains. Phylogenetic analyses were performed to identify genetic relationships among different lineages within the genotypes. With high coverage of routine immunization and intensive supplementary immunization activities, measles incidence has shown a downward trend since 1993, despite 2 resurgences, reaching a historic low level in 2020-2021 (average 0.5 per million). During 1993-2021, 9 genotypes including domestic genotype H1; imported genotypes B3, D4, D8, D9, D11, G3, and H2; and vaccine-associated genotype A were identified. Among them, the genotype H1 strain circulated endemically in China for more than 25 years; the last strain was detected in Yunnan Province in September 2019. Multiple imported genotypes have been identified since 2009 showing different transmission patterns. Since April 2020, no imported strains have been detected, while vaccine-associated genotype A continues to be detected. The evidence of low incidence during 2020-2021 and virological surveillance data in this study confirm that China is currently approaching measles elimination.

Influenza activity in the Eastern Mediterranean Region (EMR) in 2020–2021 amidst the COVID-19 pandemic

IntroductionA global reduction in influenza virus activity during the COVID-19 pandemic has been observed, including in the Eastern Mediterranean Region (EMR). However, these changes have not been thoroughly evaluated scientifically...

The effectiveness of governmental nonpharmaceutical interventions against COVID-19 at controlling seasonal influenza transmission: an ecological study

BackgroundA range of strict nonpharmaceutical interventions (NPIs) were implemented in many countries to combat the coronavirus 2019 (COVID-19) pandemic. These NPIs may also be effective at controlling seasonal influenza virus infections, as influenza viruses have the same transmission path as severe acute respiratory syndrome coronavirus 2. The aim of this study was to evaluate the effects of different NPIs on the control of seasonal influenza.MethodsData for 14 NPIs implemented in 33 countries and the corresponding influenza virological surveillance data were collected. The influenza suppression index was calculated as the difference between the influenza positivity rate during its period of decline from 2019 to 2020 and during the influenza epidemic seasons in the previous 9 years. A machine learning model was developed using an extreme gradient boosting tree regressor to fit the NPI and influenza suppression index data. The SHapley Additive exPlanations tool was used to characterize the NPIs that suppressed the transmission of influenza.ResultsOf all NPIs tested, gathering limitations had the greatest contribution (37.60%) to suppressing influenza transmission during the 2019–2020 influenza season. The three most effective NPIs were gathering limitations, international travel restrictions, and school closures. For these three NPIs, their intensity threshold required to generate an effect were restrictions on the size of gatherings less than 1000 people, ban of travel to all regions or total border closures, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask-wearing requirements and gathering limitations, whereas merely implementing a mask-wearing requirement, and not other NPIs, diluted the effectiveness of mask-wearing requirements at suppressing influenza transmission.ConclusionsGathering limitations, ban of travel to all regions or total border closures, and closing some levels of schools were found to be the most effective NPIs at suppressing influenza transmission. It is recommended that the mask-wearing requirement be combined with gathering limitations and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and other potential pandemics.

Global patterns of seasonal influenza activity, duration of activity and virus (sub)type circulation from 2010 to 2020.

BackgroundSeasonal influenza viruses undergo unpredictable changes, which may lead to antigenic mismatch between circulating and vaccine strains and to a reduced vaccine effectiveness. A continuously updated knowledge of influenza strain circulation and seasonality is essential to optimize the effectiveness of influenza vaccination campaigns. We described the global epidemiology of influenza between the 2009 A(H1N1)p and the 2020 COVID‐19 pandemic.MethodsInfluenza virological surveillance data were obtained from the WHO‐FluNet database. We determined the median proportion of influenza cases caused by the different influenza virus types, subtypes, and lineages; the typical timing of the epidemic peak; and the median duration of influenza epidemics (applying the annual average percentage method with a 75% threshold).ResultsWe included over 4.6 million influenza cases from 149 countries. The median proportion of influenza cases caused by type A viruses was 75.5%, highest in the Southern hemisphere (81.6%) and lowest in the intertropical belt (73.0%), and ranged across seasons between 60.9% in 2017 and 88.7% in 2018. Epidemic peaks typically occurred during winter months in Northern and Southern hemisphere countries, while much more variability emerged in tropical countries. Influenza epidemics lasted a median of 25 weeks (range 8–42) in countries lying between 30°N and 26°S, and a median of 9 weeks (range 5–25) in countries outside this latitude range.ConclusionsThis work will establish an important baseline to better understand factors that influence seasonal influenza dynamics and how COVID‐19 may have affected seasonal activity and influenza virus types, subtypes, and lineages circulation patterns.

COVID-19 Is Not the Flu: Four Graphs from Four Countries

Background: COVID-19 has caused a global public health emergency. In the absence of an effective vaccine, governments have implemented a series of behaviour-based prevention policies including physical distancing and preventive hygiene measures. These strategies are likely to impact the spread of other contagious respiratory illnesses, such as seasonal influenza. Our aim was to explore how the 2019-2020 influenza tracked with the COVID-19 pandemic and its mitigation methods. A secondary aim was to explore claims that the impact of COVID-19 is no different to influenza. Methods: We linked the WHO flu database (FluNet: containing influenza virological surveillance data) and COVID-19 confirmed cases (Johns Hopkins Coronavirus Resources Center) for four countries across the northern (Canada, United States) and southern hemispheres (Australia, Brazil) for the period 2016-2020. The datasets were merged and graphical presentations of the longitudinal data were provided. Findings: There was a notable reduction in influenza cases for the 2019-2020 season. Northern hemisphere countries experienced a quicker ending to the early year seasonal influenza cases (shortened by 4-7 weeks). Countries from the southern hemisphere experienced drastically low levels of seasonal influenza, with consistent trends that were approaching zero cases after the introduction of COVID-19 mitigation measures. Interpretation: COVID-19 mitigation measures are likely driving a marked decrease in influenza, including little to no 2020 influenza activity in the southern hemisphere. In spite of this reduction in influenza, there was still community spread of COVID-19, highlighting the contagiousness of SARS-CoV-2 compared to influenza. These results, together with the higher mortality rate from SARS-CoV-2 compared to influenza, provide clear evidence that the impact of COVID-19 is far greater than influenza. Another key consequence of these findings is the importance to instigate widespread influenza vaccination programmes to avoid a concomitant COVID-19 wave and influenza surge.Funding Statement: Funding for this project has come from a Canadian Institutes of Health Research-Strategy for Patient Oriented Research Mentoring Chair (SMC-151518, PI: Dr. Simon L. Bacon), a Fonds de Recherche du Québec: Santé Chair (251618, PI: Dr. Simon L. Bacon), Fonds de Recherche du Québec: Santé Senior Research Award (34757, PI: Dr. Kim L Lavoie), and a Canadian Institutes of Health Research COVID-19 grant (MS3‐ 173099, PI: Simon L. Bacon).Declaration of Interests: Dr. Bacon has received consultancy fees from Merck for the development of behavior change continuing education modules, speaker fees from Novartis and Janssen, and has served on advisory boards for Bayer, Sanofi, and Sojecci Inc none of which are related to the current article. Dr Lavoie has served on the advisory board for Schering-Plough, Takeda, AbbVie, Almirall, Janssen, GSK, Boehringer Ingelheim (BI), and Sojecci Inc, and received sponsorship for investigator-generated research grants from GlaxoSmithKline (GSK) and AbbVie, speaker fees from GSK, Astra-Zeneca, Astellas, Novartis, Takeda, AbbVie, Merck, Boehringer Ingelheim, Bayer, Pfizer and Air Liquide, and support for educational materials from Merck, none of which are related to the current article. Dr. Stojanovic and Mr. Gosselin-Boucher have nothing to declare.

2011-2019年中国B型流感季节性、年龄特征和疫苗匹配度分析

Objective: To analyze the seasonality, age distribution of influenza B cases and matching degree of influenza B vaccine in China from 2011 to 2019, and provide evidences for the future surveillance, estimation of disease burden of influenza B, application of quadrivalent vaccines, and development of vaccine strategies. Methods: The epidemiological and virological surveillance data of influenza B from week 14 of 2011 to week 13 of 2019 obtained from National Influenza Surveillance Network were used to draw hot spot maps and conduct descriptive statistics to analyze the seasonality and age distribution of influenza B cases. The published antigenicity analysis results from the China Weekly Influenza Report were used to analyze the matching degree between the trivalent vaccine strain and the circulating influenza B strains. Results: From 2011 to 2019, the incidence of influenza B showed obvious seasonal characteristics, and influenza B virus co-circulated with influenza A virus in six winter-spring seasons, and influenza B virus/Victoria and Yamagata lineages circulated alternately. In some southern provinces, two lineages co-circulated in some southern areas in certain years. The age distribution of influenza B cases was double-peaked, and both lineages had the highest positive rate in age-group 5-15 years, with peaks at age of 10 years; B/Victoria virus had a sub-peak in age-group 25-35 years; B/Yamagata virus had a sub-peak in age-group 55-65 years. Trivalent influenza vaccine strain and influenza B epidemic strains mismatched in 2015-2016 and 2017-2018 seasons, matched in 2011-2012, 2012-2013, 2013-2014, 2014-2015 and 2016-2017 seasons, and moderately matched in 2018-2019 season, but reactivity was low. Conclusions: Influenza B mainly occurred in winter-spring season in China, and its intensity was lower than that of influenza A. There was a difference in the age distribution of the cases among different virus strains. Trivalent influenza vaccine strains and influenza B epidemic strains mismatched in several seasons. It is crucial to conduct continuous surveillance of influenza B and disease burden evaluation, improve vaccine immunization strategy, increase influenza vaccination rate to reduce the harm of influenza B in high-risk groups.

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.

Influenza epidemiology and risk factors for severe acute respiratory infection in Morocco during the 2016/2017 and 2017/2018 seasons.

Introductionin order to implement an influenza vaccination program for high-risk-groups in Morocco, as recommended by the World Health Organization, an epidemiological study indicating the influenza virus effect in the development of complicated influenza for subjects with co-morbidity was required. The present study aims to evaluate the risk factors for severe acute respiratory infections caused by influenza in risk groups.Methodsthis research is based on the epidemiological and virological surveillance data of severe acute respiratory infections and influenza-like illness during the 2016/2017 and 2017/2018 seasons. It was realized using a retrospective series study with a descriptive and analytical purpose.Resultsthe over-recruitment of pediatric cases with a severe acute respiratory infection has been significantly rectified because cases of severe acute respiratory infections under 15 years old in the 2017/2018 season represent only 57.9%, whereas they represented 75.9% of the total cases of severe acute respiratory infections during the 2016/2017 season. The influenza positivity rate has increased globally and specifically by age group, clinical service and co-morbidity. The risk factors considered were significantly associated with hospitalization for influenza-associated severe acute respiratory infections. The multivariate logistic regression analysis considers male sex (OR=2.1), age ≥65 years (OR=5.4), presence of influenza cases in the surroundings (OR=0.1), diabetes (OR=7.5) and chronic respiratory disease (OR=10.9) as risk factors influenza-associated severe acute respiratory infections.Conclusionthe risk assessment of influenza-associated severe acute respiratory infections in high-risk groups revealed national epidemiological findings, particularly for diabetics and the elderly. An influenza vaccination program for these high-risk-groups becomes much recommended in Morocco.

Spread of Antigenically Drifted Influenza A(H3N2) Viruses and Vaccine Effectiveness in the United States During the 2018-2019 Season.

Increased illness due to antigenically drifted A(H3N2) clade 3C.3a influenza viruses prompted concerns about vaccine effectiveness (VE) and vaccine strain selection. We used US virologic surveillance and US Influenza Vaccine Effectiveness (Flu VE) Network data to evaluate consequences of this clade. Distribution of influenza viruses was described using virologic surveillance data. The Flu VE Network enrolled ambulatory care patients aged ≥6 months with acute respiratory illness at 5 sites. Respiratory specimens were tested for influenza by means of reverse-transcriptase polymerase chain reaction and were sequenced. Using a test-negative design, we estimated VE, comparing the odds of influenza among vaccinated versus unvaccinated participants. During the 2018-2019 influenza season, A(H3N2) clade 3C.3a viruses caused an increasing proportion of influenza cases. Among 2763 Flu VE Network case patients, 1325 (48%) were infected with A(H1N1)pdm09 and 1350 (49%) with A(H3N2); clade 3C.3a accounted for 977 (93%) of 1054 sequenced A(H3N2) viruses. VE was 44% (95% confidence interval, 37%-51%) against A(H1N1)pdm09 and 9% (-4% to 20%) against A(H3N2); VE was 5% (-10% to 19%) against A(H3N2) clade 3C.3a viruses. The predominance of A(H3N2) clade 3C.3a viruses during the latter part of the 2018-2019 season was associated with decreased VE, supporting the A(H3N2) vaccine component update for 2019-2020 northern hemisphere influenza vaccines.

A mid-term estimate of 2018/2019 vaccine effectiveness to prevent laboratory confirmed A(H1N1)pdm09 and A(H3N2) influenza cases in Sicily (Italy)

Influenza season started in Italy during the month of October 2018, approaching the epidemic peak in January 2019. This report aim to explore the mid-term virologic surveillance data of the 2018–2019 influenza season in Sicily and to estimate the effectiveness of seasonal influenza vaccine (VE) against A(H1N1)pdm09 and A(H3N2) influenza viruses. A test-negative design was used to evaluate influenza VE.In Sicily, almost all influenza infections were sustained by influenza type A viruses, of which 62.3% were A(H3N2) and 36.3% A(H1N1)pdm09. A reduction of laboratory confirmed influenza cases in Sicilian population immunized against influenza were observed. In particular, an overall significant protective values were observed for any influenza A viruses (Adj-VE = 44.0%; 95%CI: 11.2–64.7%), especially among 15–64 years old age group (Adj-VE = 59.5%; 95%CI: 0.03–83.1) and among the elderly (Adj-VE = 73.6%; 95% CI: 29.4–90.2).

Intense interseasonal influenza outbreaks, Australia, 2018/19.

BackgroundInterseasonal influenza outbreaks are not unusual in countries with temperate climates and well-defined influenza seasons. Usually, these are small and diminish before the main influenza season begins. However, the 2018/19 summer-autumn interseasonal influenza period in Australia saw unprecedented large and widespread influenza outbreaks.AimOur objective was to determine the extent of the intense 2018/19 interseasonal influenza outbreaks in Australia epidemiologically and examine the genetic, antigenic and structural properties of the viruses responsible for these outbreaks.MethodsThis observational study combined the epidemiological and virological surveillance data obtained from the Australian Government Department of Health, the New South Wales Ministry of Health, sentinel outpatient surveillance, public health laboratories and data generated by the World Health Organization Collaborating Centre for Reference and Research on Influenza in Melbourne and the Singapore Agency for Science, Technology and Research.ResultsThere was a record number of laboratory-confirmed influenza cases during the interseasonal period November 2018 to May 2019 (n= 85,286; 5 times the previous 3-year average) and also more institutional outbreaks, hospitalisations and deaths, than what is normally seen.ConclusionsThe unusually large interseasonal influenza outbreaks in 2018/19 followed a mild 2018 influenza season and resulted in a very early start to the 2019 influenza season across Australia. The reasons for this unusual event have yet to be fully elucidated but are likely to be a complex mix of climatic, virological and host immunity-related factors. These outbreaks reinforce the need for year-round surveillance of influenza, even in temperate climates with strong seasonality patterns.

National Surveillance for Health-Related Workplace Absenteeism, United States 2017-18

National Surveillance for Health-Related Workplace Absenteeism, United States 2017-18

Birth Cohort Effects in Influenza Surveillance Data: Evidence That First Influenza Infection Affects Later Influenza-Associated Illness.

The evolution of influenza A viruses results in birth cohorts that have different initial influenza virus exposures. Historically, A/H3 predominant seasons have been associated with more severe influenza-associated disease; however, since the 2009 pandemic, there are suggestions that some birth cohorts experience more severe illness in A/H1 predominant seasons. United States influenza virologic, hospitalization, and mortality surveillance data during 2000-2017 were analyzed for cohorts born between 1918 and 1989 that likely had different initial influenza virus exposures based on viruses circulating during early childhood. Relative risk/rate during H3 compared with H1 predominant seasons during prepandemic versus pandemic and later periods were calculated for each cohort. During the prepandemic period, all cohorts had more influenza-associated disease during H3 predominant seasons than H1 predominant seasons. During the pandemic and later period, 4 cohorts had higher hospitalization and mortality rates during H1 predominant seasons than H3 predominant seasons. Birth cohort differences in risk of influenza-associated disease by influenza A virus subtype can be seen in US influenza surveillance data and differ between prepandemic and pandemic and later periods. As the population ages, the amount of influenza-associated disease may be greater in future H1 predominant seasons than H3 predominant seasons.

European all-cause excess and influenza-attributable mortality in the 2017/18 season: should the burden of influenza B be reconsidered?

ObjectivesWeekly monitoring of European all-cause excess mortality, the EuroMOMO network, observed high excess mortality during the influenza B/Yamagata dominated 2017/18 winter season, especially among elderly. We describe all-cause excess and influenza-attributable mortality during the season 2017/18 in Europe. MethodsBased on weekly reporting of mortality from 24 European countries or sub-national regions, representing 60% of the European population excluding the Russian and Turkish parts of Europe, we estimated age stratified all-cause excess morality using the EuroMOMO model. In addition, age stratified all-cause influenza-attributable mortality was estimated using the FluMOMO algorithm, incorporating influenza activity based on clinical and virological surveillance data, and adjusting for extreme temperatures. ResultsExcess mortality was mainly attributable to influenza activity from December 2017 to April 2018, but also due to exceptionally low temperatures in February-March 2018. The pattern and extent of mortality excess was similar to the previous A(H3N2) dominated seasons, 2014/15 and 2016/17. The 2017/18 overall all-cause influenza-attributable mortality was estimated to be 25.4 (95%CI 25.0-25.8) per 100,000 population; 118.2 (116.4-119.9) for persons aged 65. Extending to the European population this translates into over-all 152,000 deaths. ConclusionsThe high mortality among elderly was unexpected in an influenza B dominated season, which commonly are considered to cause mild illness, mainly among children. Even though A(H3N2) also circulated in the 2017/18 season and may have contributed to the excess mortality among the elderly, the common perception of influenza B only having a modest impact on excess mortality in the older population may need to be reconsidered.

Epidemiology of seasonal influenza in the Middle East and North Africa regions, 2010-2016: Circulating influenza A and B viruses and spatial timing of epidemics.

BackgroundThere is a limited knowledge regarding the epidemiology of influenza in Middle East and North Africa.ObjectivesWe described the patterns of influenza circulation and the timing of seasonal epidemics in countries of Middle East and North Africa.MethodsWe used virological surveillance data for 2010‐2016 from the WHO FluNet database. In each country, we calculated the median proportion of cases that were caused by each virus type and subtype; determined the timing and amplitude of the primary and secondary peaks; and used linear regression models to test for spatial trends in the timing of epidemics.ResultsWe included 70 532 influenza cases from seventeen countries. Influenza A and B accounted for a median 76.5% and 23.5% of cases in a season and were the dominant type in 86.8% and 13.2% of seasons. The proportion of influenza A cases that were subtyped was 85.9%, while only 4.4% of influenza B cases were characterized. For most countries, influenza seasonality was similar to the Northern Hemisphere, with a single large peak between January and March; exceptions were the countries in the Arabian Peninsula and Jordan, all of which showed clear secondary peaks, and some countries had an earlier primary peak (in November‐December in Bahrain and Qatar). The direction of the timing of influenza activity was east to west and south to north in 2012‐2013 and 2015‐2016, and west to east in 2014‐2015.ConclusionsThe epidemiology of influenza is generally uniform in countries of Middle East and North Africa, with influenza B playing an important role in the seasonal disease burden.