Flu vaccine uptake (percent) in at risk individuals aged 6 months to 65 years (excluding pregnant women), who received the flu vaccination between 1st September to the end of February as recorded in the GP record. The February collection has been adopted for our end of season figures from 2017 to 2018. All previous data is the same definitions but until the end of January rather than February to consider data returning from outside the practice and later in practice vaccinations.RationaleInfluenza (also known as Flu) is a highly infectious viral illness spread by droplet infection. The flu vaccination is offered to people who are at greater risk of developing serious complications if they catch the flu. The seasonal influenza programme for England is set out in the Annual Flu Letter. Both the flu letter and the flu plan have the support of the Chief Medical Officer (CMO), Chief Pharmaceutical Officer (CPhO), and Director of Nursing.Vaccination coverage is the best indicator of the level of protection a population will have against vaccine-preventable communicable diseases. Immunisation is one of the most effective healthcare interventions available, and flu vaccines can prevent illness and hospital admissions among these groups of people. Increasing the uptake of the flu vaccine among these high-risk groups should also contribute to easing winter pressure on primary care services and hospital admissions. Coverage is closely related to levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise.The UK Health Security Agency (UKHSA) will continue to provide expert advice and monitoring of public health, including immunisation. NHS England now has responsibility for commissioning the flu programme, and GPs continue to play a key role. NHS England teams will ensure that robust plans are in place locally and that high vaccination uptake levels are reached in the clinical risk groups. For more information, see the Green Book chapter 19 on Influenza.The Annual Flu Letter sets out the national vaccine uptake ambitions each year. In 2021 to 2022, the national ambition was to achieve at least 85 percent vaccine uptake in those aged 65 and over. Prior to this, the national vaccine uptake ambition was 75 percent, in line with WHO targets.Definition of numeratorNumerator is the number of vaccinations administered during the influenza season between 1st September and the end of February.Definition of denominatorDenominator is the GP registered population on the date of extraction including patients who have been offered the vaccine but refused it, as the uptake rate is measured against the overall eligible population. For more detailed information please see the user guide, available to view and download from https://www.gov.uk/government/collections/vaccine-uptake#seasonal-flu-vaccine-uptakeCaveatsRead codes are primarily used for data collection purposes to extract vaccine uptake data for patients who fall into one or more of the designated clinical risk groups. The codes identify individuals at risk, and therefore eligible for flu vaccination. However, it is important to note that there may be some individuals with conditions not specified in the recommended risk groups for vaccination, who may be offered influenza vaccine by their GP based on clinical judgement and according to advice contained in the flu letter and Green Book, and thus are likely to fall outside the listed Read codes. Therefore, this data should not be used for GP payment purposes.

Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs due to high densities of disparate populations of long distance migratory and native bird species present during breeding seasons. Iceland is a staging ground that connects the East Atlantic and North Atlantic American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. Findings demonstrate that IAV movement in the arctic and subarctic follows seabird migration around the perimeter of the circumpolar north, favoring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, particularly due to the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, gulls act as recipients and not sources of IAVs to other avian hosts prior to onward migration. These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with pandemic potential. Methods Field sample collection From May 2010 through February 2018, we obtained IAV isolates from various species of seabirds, shorebirds, and waterfowl as well as environmental sampling of avian fecal material from locations throughout Iceland (capture and swab data can be found here: https://doi.org/10.5066/XX (Dusek et al. 202X)). Live sampled birds were captured using a 18m x 12m cannon-propelled capture net, noose pole, or hand capture. Birds found dead or moribund were also sampled. Hunter-harvested waterfowl and fisheries-bycatch seabirds were sampled as available. All birds were identified to species and, for live birds, individually marked with metal bands. Age characteristics were determined and age was documented for each bird according to the following schemes adapted from U.S. Geological Survey year classification codes: hatched in same calendar year as sampling (1CY), hatched previous calendar year (2CY), hatched previous calendar year or older, exact age unknown (2CY+), hatched three calendar years prior to sampling (3CY), hatched four calendar years prior to sampling (4CY), hatched more than four calendar years prior to sampling (4CY+), or unknown if age could not be determined (U) (Olsen KM, 2004; Prater, Marchant, & Vuorinen, 1977; USGS, 2020). Due to species specific differences, not all aging categories could be applied to all species sampled. All live birds were immediately released following completion of sampling. To sample for IAV, a single polyester-tipped swab was used to swab the cloaca only (2010-2013) or to first swab the oral cavity then the cloaca (2014-2017). Opportunistic environmental sampling of fecal material was also conducted using a direct swabbing method (2018). Each swab sample was immediately placed in individual cryovials containing 1.25 ml viral transport media (Docherty & Slota, 1988). Vials were held on ice for up to 5 hours prior to being stored in liquid nitrogen or liquid nitrogen vapor. Samples were shipped on dry ice from Iceland to Madison, Wisconsin, USA by private courier with dry ice replenishment during shipping. Once received in the laboratory, samples were stored at -80o C until analysis. Virus extraction, RT-PCR, virus isolation Viral RNA was extracted from swab samples using the MagMAXTM-96 AI/ND Viral RNA Isolation Kit (Ambion, Austin, TX) following the manufacturer’s procedures. Real-time RT-PCR was performed using previously published procedures, primers, and probes (Spackman et al., 2002) designed to detect the IAV matrix gene. RT-PCR assays utilized reagents provided in the Qiagen OneStep® RT-PCR kit. Virus isolation was performed in embryonating chicken egg culture on all swab samples exhibiting positive Ct values from RT-PCR analysis (Woolcock, 2008), with a primary cut off value of 45 on primary screen and 22 on secondary screen. All virus isolates were screened for the presence of H5 and H7 IAV subtypes using primers and probes specific for those subtypes (Spackman et al., 2002). Egg-grown virus isolates were sequenced using multiple standard methods including Sanger, Roche 454, and Illumina (HiSeq 2000 and MiSeq) sequencing (Dusek et al., 2014; Guan et al., 2019; Hall et al., 2014). Datasets for phylodynamic analyses Global dataset: The PB2 segment was selected as the basis for phylodynamic analysis. Advantages of focusing on PB2 - the largest internal segment of the IAV genome - include maximizing the number of nucleotides in the analysis (>2000 nts) and investigating transmission dynamics without targeting a specific subtype. All available avian and marine mammal IAV PB2 genes sequenced between 2009 and 2019 globally were downloaded from the National Center for Biotechnology Information Influenza Virus Resource database (NCBI IVR) (http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html) on February 12, 2020, resulting in 13,469 sequences. Duplicate sequences (based on collection date, location, and nucleotide content) and sequences with less than 75% unambiguous bases were removed, and all vaccine derivative and laboratory-synthesized recombinant sequences were excluded. Sequences in the dataset were only included if isolation dates, location, and host species were available, resulting in 7,245 remaining sequences. Downsampling of taxa: The downsampling strategy aimed to reduce the number of sequence taxa for computation and mitigate sampling bias while maintaining the genetic diversity in the dataset. Four variables were considered important for explaining genetic diversity in the IAV sequence dataset: geographic region, host taxa, sampling year, and hemagglutinin (HA) subtype. Geographic regions included North America, Europe, Iceland, Asia, Africa, and South America (Australia and Antarctica were removed due to insufficient sequence counts). HA subtypes included H1, H2, H3, H4, H6, H8, H10, H11, H12, H13, H14, H15, H16, and pooled H5/7/9. H5, H7, and H9 were combined, as these were over-represented in the global dataset. Host categories included Anseriformes, Charadriiformes, Galliformes, and Other, which comprised all other avian taxa and marine mammals. To inform the downsampling strategy and evaluate if any of the four variables were correlated, a multiple correspondence analysis (MCA) was performed (JMP Pro v.14.0.0 (JMP Version 14.0.0, 1989-2019)). The MCA uses categorical data as input, which for this study included the sampling metadata associated with each sequence (region, host taxa, year, and HA subtype). Through representation of the variables in two-dimensional Euclidean space, significant clustering of HA subtypes with host taxa was detected (Supplementary Fig. 1), indicative of host-specific subtypes that are a well-known feature of influenza. These findings confirmed by previously published data on species-specificity of HA subtypes (Byrd-Leotis, Cummings, & Steinhauer, 2017; Long, Mistry, Haslam, & Barclay, 2019; Verhagen et al., 2015) led us to downsample the dataset stratifying taxa by two non-overlapping variables: geographic region and HA subtype. Data were downsampled to maintain 21-75 taxa per geographic region category and 6-30 per HA subtype category, resulting in a total of 301 sequences (outgroup). This step was performed to mitigate sampling bias resulting in over-representation of species or viral strains, while accounting for genetic diversity in the dataset. Next, to ensure relative evenness of geographic state groupings for discrete trait analyses, virus sequences from Iceland (n=93) were downsampled by stratifying taxa by HA subtype and maintaining 1-15 sequences per category, resulting in 63 sequences (ingroup). These 63 sequences were used for global and local discrete trait analyses and reflected the composition of diverse subtypes by host for the full Iceland sequence dataset. The resulting dataset reflected the underlying composition of host-specific subtypes present in this localized system. To assist with rooting and time-calibration of the tree, historical avian sequences from NCBI IVR were downloaded for the years 1979-2008. These were downsampled by year to ensure one sequence per year, resulting in 30 historic sequences. The total downsampled dataset, including the outgroup (n=301), ingroup (n=63), and historic sequences (n=30) resulted in a total of 394 sequences. Europe-Iceland-North America Datasets: To elucidate viral dynamics between significant source regions and Iceland and within-Iceland phylodynamics, a second analysis was performed at a restricted scale to Europe, Iceland, and North America. The cleaned global dataset described above (n=7245) was downsampled to include significant source regions of North America (n=3222) and Europe (n=407), totaling 3629 sequences. To identify at lower spatial resolution the source/sink locations relevant to Iceland, a K-means cluster analysis was performed (JMP Pro v.14.0.0 (JMP Version 14.0.0, 1989-2019)) using latitude/longitude coordinates for each of the 3629 sequences (obtained by extracting sampling location from the strain name of each sequence and searching in www.geonames.org). A total of 20 intraregional clusters resulted in highest support. Identified clusters with <50 sequences were combined with geographically proximal

BackgroundsElectronic medical records (EMR) form a rich repository of information that could benefit public health. We asked how structured and free-text narrative EMR data should be combined to improve epidemic surveillance for acute respiratory infections (ARI).MethodsEight previously characterized ARI case detection algorithms (CDA) were applied to historical EMR entries to create authentic time series of daily ARI case counts (background). An epidemic model simulated influenza cases (injection). From the time of the injection, cluster-detection statistics were applied daily on paired background+injection (combined) and background-only time series. This cycle was then repeated with the injection shifted to each week of the evaluation year. We computed: a) the time from injection to the first statistical alarm uniquely found in the combined dataset (Detection Delay); b) how often alarms originated in the background-only dataset (false-alarm rate, or FAR); and c) the number of cases found within these false alarms (Caseload). For each CDA, we plotted the Detection Delay as a function of FAR or Caseload, over a broad range of alarm thresholds.ResultsCDAs that combined text analyses seeking ARI symptoms in clinical notes with provider-assigned diagnostic codes in order to maximize the precision rather than the sensitivity of case-detection lowered Detection Delay at any given FAR or Caseload.ConclusionAn empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection.

Flu vaccine uptake (%) in school aged children from Reception to Year 6 (age 4 to 11 year olds) between 1st September to the end of January.RationaleInfluenza (also known as Flu) is a highly infectious viral illness spread by droplet infection. The flu vaccination is offered to people who are at greater risk of developing serious complications if they catch the flu. The seasonal influenza programme for England is set out in the Annual Flu Letter. Both the flu letter and the flu plan have the support of the Chief Medical Officer (CMO), Chief Pharmaceutical Officer (CPhO), and Director of Nursing.Vaccination coverage is the best indicator of the level of protection a population will have against vaccine-preventable communicable diseases. Immunisation is one of the most effective healthcare interventions available, and flu vaccines can prevent illness and hospital admissions among these groups of people. Increasing the uptake of the flu vaccine among these high-risk groups should also contribute to easing winter pressure on primary care services and hospital admissions. Coverage is closely related to levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise.The UK Health Security Agency (UKHSA) will continue to provide expert advice and monitoring of public health, including immunisation. NHS England now has responsibility for commissioning the flu programme, and GPs continue to play a key role. NHS England teams will ensure that robust plans are in place locally and that high vaccination uptake levels are reached in the clinical risk groups. For more information, see the Green Book chapter 19 on Influenza.The Annual flu letter sets out the national vaccine uptake ambitions each year. In 2021 to 2022, the national ambition was to achieve at least 70% vaccine uptake in school aged children in Reception to Year 6 (age 4 to 11 years old).Definition of numeratorThe total number of children in the respective eligible age cohort that have received at least one dose of influenza vaccine from 1 September in school, pharmacy, and general practice.Definition of denominatorThe total number of children eligible for influenza vaccination in the LA geography and children educated out of school in the LA geography, defined by child age on 31 AugustCaveatsData for ICBs are estimated from local authority data. In most cases, ICBs are coterminous with local authorities, so the ICB figures are precise. In cases where local authorities cross ICB boundaries, the local authority data are proportionally split between ICBs, based on the population located in each ICB.The affected ICBs are:Bath and North East Somerset, Swindon and WiltshireBedfordshire, Luton and Milton KeynesBuckinghamshire, Oxfordshire and Berkshire WestCambridgeshire and PeterboroughFrimleyHampshire and Isle of WightHertfordshire and West EssexHumber and North YorkshireLancashire and South CumbriaNorfolk and WaveneyNorth East and North CumbriaSuffolk and North East EssexSurrey HeartlandsSussexWest YorkshireRead codes are primarily used for data collection purposes to extract vaccine uptake data for patients who fall into one or more of the designated clinical risk groups. The codes identify individuals at risk and therefore eligible for flu vaccination. However, it is important to note that there may be some individuals with conditions not specified in the recommended risk groups for vaccination, who may be offered influenza vaccine by their GP based on clinical judgement and according to advice contained in the flu letter and Green Book, and thus may fall outside the listed read codes. Therefore, it is important to note that for the reasons mentioned, this data should not be used for GP payment purposes.This collection is regularly submitted for approval from the Data Coordination Board (DCB).

A significant reduction in influenza incidence during the early days of COVID-19 pandemic was reported worldwide. This study aims to understand the impact of public health and social measures implemented during the COVID- 19 pandemic on influenza circulation in Nepal. We utilized influenza sentinel and non-sentinel surveillance data from Nepal between 2018 and 2022, obtained from the National Influenza Centre (NIC) at National Public Health Laboratory (NPHL), Nepal. Additionally, we used publicly available national COVID-19 case data released by the Ministry of Health and Population of Nepal. The data were analyzed for the trends in influenza cases, positivity rate and the distribution of subtypes/lineages. Furthermore, we compared the trend of influenza with that of COVID-19 and the social and public health measures implemented in the country as part of the COVID-19 response. The average influenza positivity rate dropped significantly from 39% to 14% during the COVID-19 period compared to the pre- COVID-19. Additionally, during the time of COVID 19 there has been a shift in the influenza bimodal seasonal pattern, with only one peak observed. Influenza type A consistently dominated, with variations in its subtype observed from year to year. Notably, one case of Influenza A/H5N1 was reported in 2019. This study shows that the influenza positivity rate decreased substantially after the COVID-19 pandemic began, possibly due to the stringent public health and social measures implemented during the pandemic. Adaptation of the influenza surveillance system during pandemics and integration of other respiratory pathogens into it not only allows continuity of surveillance but also helps to evaluate the public health and social measures implemented to manage future respiratory virus pandemics.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.