Abstract:The dynamics of viral shedding and symptoms following influenza virus infection are key factors when considering epidemic control measures. The authors reviewed published studies describing the course of influenza virus infection in placebo-treated and untreated volunteers challenged with wild-type influenza virus. A total of 56 different studies with 1,280 healthy participants were considered. Viral shedding increased sharply between 0.5 and 1 day after challenge and consistently peaked on day 2. The duration… Show more
“…What this study adds is the additional information of the comprehensive burden of all‐influenza cases in primary care, be they mild or more severe, not only by age, but also by type/subtype, over a long time period. It is interesting that the “typical” pattern of influenza B shows a substantially higher relative iMAARI attack rate among school‐age children5, 6, 7, 8, 9, 10, 11, 12, 13, 14 compared to that in age group 0‐4 (Figure 5, right panel), although in absolute terms the attack rate among 5‐ to 14‐year‐old children is comparable to that caused by A(H3N2). This striking characteristic of influenza B concurs with data from two serological studies, one from the Netherlands and one from Germany, which investigated the seroprevalence of antibodies against influenza virus types and subtypes by year of age among children 17, 18.…”
Section: Discussionmentioning
confidence: 96%
“…Similarly, in the three seasons following the pandemic, influenza‐associated consultations by patients with ILI were estimated in a population‐based surveillance project in 13 US health jurisdictions as 0.7%, 0.2% and 1.1% 16. Given that only between 30% and 80% of all influenza cases manifest themselves as ILI5, 6, 7, 8 and in the same year influenza seasons may be quite different in different countries, the estimated 2.6%, 1.0% and 8.9% in our study lie in a comparable magnitude as the US data. We believe that our combination of surveillance (using ARI data) followed by modelling estimates the population impact of influenza more realistically than sentinel systems that use ILI data.…”
Section: Discussionmentioning
confidence: 99%
“…As a result, ILI case definitions are more specific, but less sensitive compared to ARI, and as a corollary surveillance systems using ILI see more pronounced illness waves and peaks during influenza epidemics. However, because only a portion of all symptomatic influenza cases are captured by ILI case definitions,1, 5, 6, 7, 8, 9 ILI surveillance systems are less well suited to describe and capture the burden of disease of influenza.…”
BackgroundThe total burden of influenza in primary care is difficult to assess. The case definition of medically attended “acute respiratory infection” (MAARI) in the German physician sentinel is sensitive; however, it requires modelling techniques to derive estimates of disease attributable to influenza. We aimed to examine the impact of type/subtype and age.MethodsData on MAARI and virological results of respiratory samples (virological sentinel) were available from 2001/02 until 2014/15. We constructed a generalized additive regression model for the periodic baseline and the secular trend. The weekly number of influenza‐positive samples represented influenza activity. In a second step, we distributed the estimated influenza‐attributable MAARI (iMAARI) according to the distribution of types/subtypes in the virological sentinel.ResultsSeason‐specific iMAARI ranged from 0.7% to 8.9% of the population. Seasons with the strongest impact were dominated by A(H3), and iMAARI attack rate of the pandemic 2009 (A(H1)pdm09) was 4.9%. Regularly the two child age groups (0‐4 and 5‐14 years old) had the highest iMAARI attack rates reaching frequently levels up to 15%‐20%. Influenza B affected the age group of 5‐ to 14‐year‐old children substantially more than any other age group. Sensitivity analyses demonstrated both comparability and stability of the model.ConclusionWe constructed a model that is well suited to estimate the substantial impact of influenza on the primary care sector. A(H3) causes overall the greatest number of iMAARI, and influenza B has the greatest impact on school‐age children. The model may incorporate time series of other pathogens as they become available.
“…What this study adds is the additional information of the comprehensive burden of all‐influenza cases in primary care, be they mild or more severe, not only by age, but also by type/subtype, over a long time period. It is interesting that the “typical” pattern of influenza B shows a substantially higher relative iMAARI attack rate among school‐age children5, 6, 7, 8, 9, 10, 11, 12, 13, 14 compared to that in age group 0‐4 (Figure 5, right panel), although in absolute terms the attack rate among 5‐ to 14‐year‐old children is comparable to that caused by A(H3N2). This striking characteristic of influenza B concurs with data from two serological studies, one from the Netherlands and one from Germany, which investigated the seroprevalence of antibodies against influenza virus types and subtypes by year of age among children 17, 18.…”
Section: Discussionmentioning
confidence: 96%
“…Similarly, in the three seasons following the pandemic, influenza‐associated consultations by patients with ILI were estimated in a population‐based surveillance project in 13 US health jurisdictions as 0.7%, 0.2% and 1.1% 16. Given that only between 30% and 80% of all influenza cases manifest themselves as ILI5, 6, 7, 8 and in the same year influenza seasons may be quite different in different countries, the estimated 2.6%, 1.0% and 8.9% in our study lie in a comparable magnitude as the US data. We believe that our combination of surveillance (using ARI data) followed by modelling estimates the population impact of influenza more realistically than sentinel systems that use ILI data.…”
Section: Discussionmentioning
confidence: 99%
“…As a result, ILI case definitions are more specific, but less sensitive compared to ARI, and as a corollary surveillance systems using ILI see more pronounced illness waves and peaks during influenza epidemics. However, because only a portion of all symptomatic influenza cases are captured by ILI case definitions,1, 5, 6, 7, 8, 9 ILI surveillance systems are less well suited to describe and capture the burden of disease of influenza.…”
BackgroundThe total burden of influenza in primary care is difficult to assess. The case definition of medically attended “acute respiratory infection” (MAARI) in the German physician sentinel is sensitive; however, it requires modelling techniques to derive estimates of disease attributable to influenza. We aimed to examine the impact of type/subtype and age.MethodsData on MAARI and virological results of respiratory samples (virological sentinel) were available from 2001/02 until 2014/15. We constructed a generalized additive regression model for the periodic baseline and the secular trend. The weekly number of influenza‐positive samples represented influenza activity. In a second step, we distributed the estimated influenza‐attributable MAARI (iMAARI) according to the distribution of types/subtypes in the virological sentinel.ResultsSeason‐specific iMAARI ranged from 0.7% to 8.9% of the population. Seasons with the strongest impact were dominated by A(H3), and iMAARI attack rate of the pandemic 2009 (A(H1)pdm09) was 4.9%. Regularly the two child age groups (0‐4 and 5‐14 years old) had the highest iMAARI attack rates reaching frequently levels up to 15%‐20%. Influenza B affected the age group of 5‐ to 14‐year‐old children substantially more than any other age group. Sensitivity analyses demonstrated both comparability and stability of the model.ConclusionWe constructed a model that is well suited to estimate the substantial impact of influenza on the primary care sector. A(H3) causes overall the greatest number of iMAARI, and influenza B has the greatest impact on school‐age children. The model may incorporate time series of other pathogens as they become available.
“…Patient records with missing RT‐PCR results or no report of fever were excluded from analysis. Patients with specimens collected more than 5 days after onset were also excluded due to a loss of viral detectability after 5 days
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Please cite this paper as: Fowlkes et al. (2012) Estimating influenza incidence and rates of influenza‐like illness in the outpatient setting. Influenza and Other Respiratory Viruses DOI: 10.1111/irv.12014.
Background: Estimating influenza incidence in outpatient settings is challenging. We used outpatient healthcare practice populations as a proxy to estimate community incidence of influenza‐like illness (ILI) and laboratory‐confirmed influenza‐associated ILI.
Methods: From October 2009 to July 2010, 38 outpatient practices in seven jurisdictions conducted surveillance for ILI (fever with cough or sore throat for patients ≥2 years; fever with ≥1 respiratory symptom for patients <2 years). From a sample of patients with ILI, respiratory specimens were tested for influenza.
Results: During the week of peak influenza activity (October 24, 2009), 13% of outpatient visits were for ILI and influenza was detected in 72% of specimens. For the 10‐month surveillance period, ILI and influenza‐associated ILI incidence were 20·0 (95% CI: 19·7, 20·4) and 8·7/1000 (95% CI: 8·2, 9·2) persons, respectively. Influenza‐associated ILI incidence was highest among children aged 2–17 years. Observed trends were highly correlated with national ILI and virologic surveillance.
Conclusions: This is the first multistate surveillance system demonstrating the feasibility of using outpatient practices to estimate the incidence of medically attended influenza at the community level. Surveillance demonstrated the substantial burden of pandemic influenza in outpatient settings and especially in children aged 2–17 years. Observed trends were consistent with established syndromic and virologic systems.
“…Studies have shown controversial findings of clinical features in cases infected with A and B infections 21, 22, 23. In both paediatric and adult populations, patients with influenza B infection showed similar clinical features compared with influenza A 22, 24, 25, 26.…”
BackgroundInfluenza B is characterised by two antigenic lineages: B/Victoria and B/Yamagata. These lineages circulate together with influenza A during influenza seasons, with varying incidence from year to year and by geographic region.ObjectiveTo determine the epidemiology of influenza B relative to influenza A in Australia.MethodsLaboratory‐confirmed influenza notifications between 2001 and 2014 in Australia were obtained from the Australian National Notifiable Diseases Surveillance System.ResultsA total of 278 485 laboratory‐confirmed influenza cases were notified during the study period, comprising influenza A (82.2%), B (17.1%) and ‘other and untyped’ (0.7%). The proportion of notifications that were influenza B was highest in five‐ to nine‐year‐olds (27.5%) and lowest in persons aged 85 years and over (11.5%). Of all B notifications with lineage determined, 77.1% were B/Victoria and 22.9% were B/Yamagata infections. Mismatches between the dominant B lineage in a season and the trivalent vaccine B lineage occurred in over one‐third of seasons during the study years. In general, influenza B notifications peaked later than influenza A notifications.ConclusionThe proportion of circulating influenza B in Australia during 2001‐2014 was slightly lower than the global average and was dominated by B/Victoria. Compared with influenza A, influenza B infection was more common among older children and young adults and less common in the very elderly. Influenza B lineage mismatch with the trivalent vaccine occurred about one‐third of the time.
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