Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

Goldstein, Édward; Cobey, Sarah; Takahashi, Saki; Miller, Joel C.; Lipsitch, Marc

doi:10.1371/journal.pmed.1001051

Cited by 178 publications

(194 citation statements)

References 10 publications

Supporting

Mentioning

182

Contrasting

Order By: Relevance

“…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. Both studies showed that seroprevalence of antibodies against influenza A viruses rises faster in early childhood compared to influenza B.…”

Section: Discussionsupporting

confidence: 83%

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Heiden

Buchholz

2016

Influenza Resp Viruses

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 83%

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Heiden

Buchholz

2016

Influenza Resp Viruses

View full text Add to dashboard Cite

show abstract

“…Rather, our results suggest that third waves of A(H1N1)pdm09 may have been caused by not-yet-characterized large susceptible populations at the end of initial pandemic waves, possibly in addition to other explanations, such as increased intrinsic transmissibility (19). Furthermore, if similar patterns of antibody persistence occur during the interpandemic period, they may explain the apparent cycling of H1N1 and H3N2 subtypes (20) and consequently the persistence of H1N1 as a minor subtype. A caveat to these observations is that our study was restricted to healthy adults and therefore the findings may not extend to children, the elderly, or other groups at higher risk of generating clinical cases.…”

Section: Discussionmentioning

confidence: 70%

Longevity and Determinants of Protective Humoral Immunity after Pandemic Influenza Infection

Sridhar

Begom

Höschler

et al. 2015

Am J Respir Crit Care Med

View full text Add to dashboard Cite

Rationale: Antibodies to influenza hemagglutinin are the primary correlate of protection against infection. The strength and persistence of this immune response influences viral evolution and consequently the nature of influenza epidemics. However, the durability and immune determinants of induction of humoral immunity after primary influenza infection remain unclear.Objectives: The spread of a novel H1N1 (A[H1N1]pdm09) virus in 2009 through an unexposed population offered a natural experiment to assess the nature and longevity of humoral immunity after a single primary influenza infection.Methods: We followed A(H1N1)pdm09-seronegative adults through two influenza seasons (2009-2011) as they developed A(H1N1)pdm09 influenza infection or were vaccinated. Antibodies to A(H1N1)pdm09 virus were measured by hemagglutinationinhibition assay in individuals with paired serum samples collected preinfection and postinfection or vaccination to assess durability of humoral immunity. Preexisting A(H1N1)pdm09-specific multicytokine-secreting CD4 and CD8 T cells were quantified by multiparameter flow cytometry to test the hypothesis that higher frequencies of CD4 1 T-cell responses predict stronger antibody induction after infection or vaccination.Measurements and Main Results: Antibodies induced by natural infection persisted at constant high titer for a minimum of approximately 15 months. Contrary to our initial hypothesis, the fold increase in A(H1N1)pdm09-specific antibody titer after infection was inversely correlated to the frequency of preexisting circulating A(H1N1)pdm09-specific CD4 1 IL-2 1 IFN-g 2 TNF-a 2 T cells (r = 20.4122; P = 0.03).Conclusions: The longevity of protective humoral immunity after influenza infection has important implications for influenza transmission dynamics and vaccination policy, and identification of its predictive cellular immune correlate could guide vaccine development and evaluation.

show abstract

“…During the 2009 A/H1N1 pandemic, which occurred at an unusual time of the year when circulation of other respiratory pathogens was limited, the proportion of ILI caused by an influenza virus reached 40-60% [79,80]. The bias introduced by non-influenza pathogens can be reduced by relying on timing of peak ILI incidence, or on ILI rates in excess of a seasonal baseline, which are highly correlated with viral activity data, especially in influenza A/H3N2 seasons [15,81,82]. However, in contrast to molecular studies, epidemiological data cannot provide more detailed information on the spread of individual viral lineages that may differ in major phenotypic properties such as antigenicity [2,25] and capacity for drug resistance [83,84].…”

Section: Discussionmentioning

confidence: 99%

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

Viboud

Nelson

Tan

et al. 2013

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

One contribution of 18 to a Discussion Meeting Issue 'Next-generation molecular and evolutionary epidemiology of infectious disease'. In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of interregional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.

show abstract

Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

Cited by 178 publications

References 10 publications

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Longevity and Determinants of Protective Humoral Immunity after Pandemic Influenza Infection

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

Contact Info

Product

Resources

About