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Asymptomatic Influenza Infection Rates Deserve More Attention

Although asymptomatic influenza infection rates are an important component of models used to predict influenza outbreaks, these rates may be arbitrary, contributing to significant heterogeneity within assessments of asymptomatic and subclinical influenza prevalence. The ramifications of such heterogeneity can result in suboptimal intervention planning for pandemic and interpandemic influenza, which are derived from models and simulations that include arbitrary estimates of asymptomatic infection in the range of 30%–50%.

Despite long-standing knowledge of the occurrence of asymptomatic influenza infection,1 its role in viral transmission has been understudied.2,3 The consequences of not understanding the actual rate of asymptomatic influenza infection can be grave, as influenza virus infection of the respiratory tract has been shown to result in severe disease and complications, including pneumonia, shock, renal failure, encephalopathy, and multiorgan dysfunction, which can be lethal.4-6 The results of a social network analysis provide an excellent example of the importance of understanding rates of asymptomatic influenza infection, as nearly one third of the attack rate for the influenza A(H1N1)pdm09 virus epidemic in England from 2009 to 2010 was attributable to asymptomatic infection.7

In order to determine the prevalence of asymptomatic influenza infection and identify any factors associated with the heterogeneity reported across studies, lead author Luis Furuya-Kanamori, a PhD student from the Acton Campus of the Australian National University in the Australian Capital Territory, and colleagues conducted a systematic review and meta-analysis of studies on the prevalence of asymptomatic versus subclinical carriers among persons with laboratory-confirmed influenza.8 In explaining the rationale for their study, Furuya-Kanamori et al stated, "... assigning an arbitrary value for asymptomatic infection rates that does not reflect this heterogeneity presents an important shortcoming in the current ability to accurately predict influenza outbreaks."

Study results revealed an overall pooled prevalence for asymptomatic carriers of 19.1% for any type of influenza, 21.0% for influenza A, and 22.7% for influenza A(H1N1). For subclinical carriers, the overall pooled prevalence of was found to be 43.4% for any type of influenza, 42.8% for influenza A, and 39.8% for influenza A(H1N1).
Furthermore, Furuya-Kanamori et al reported finding very high levels of heterogeneity for the reported asymptomatic and subclinical prevalences that were not attributable to the influenza type or subtype alone. This heterogeneity could not be explained by other evaluated factors including the laboratory tests used to detect the virus, the location where the study was conducted, or the year of the study. The multivariate regression models employed by the investigators only accounted for 16.8% and 14.8% of the variance observed for the asymptomatic and subclinical prevalences, respectively.

As a description of the conclusions that could be drawn from the data, Furuya-Kanamori and colleagues stated, "We found no evidence to support a fixed asymptomatic rate (or even an informative range) between or even within influenza virus subtypes. For example, the prevalence of asymptomatic influenza A(H1N1) virus ranged from 0% to 65%, resulting in an overall failure to explain the extreme heterogeneity in this reported rate." Furthermore, they suggested that "...  the term 'asymptomatic' be used exclusively to describe the complete absence of symptoms associated with influenza virus infection in patients with laboratory-confirmed cases."

In describing the broader implications of their findings, Furuya-Kanamori et al stated, "As new subtypes and strains emerge, actively surveying infection status of local populations and tracking any changes in asymptomatic rates of infection should increasingly become a global health priority, possibly necessitating the provision of international resources and the deployment of dedicated rapid-response teams who are guided by standardized protocols." Based on the results of this study, it would seem that a much greater level of attention will be required in order to establish reliable estimates of the prevalence of asymptomatic influenza infection needed to derive accurate predictions of influenza outbreaks.
William Perlman, PhD, CMPP is a former research scientist currently working as a medical/scientific content development specialist. He earned his BA in Psychology from Johns Hopkins University, his PhD in Neuroscience at UCLA, and completed three years of postdoctoral fellowship in the Neuropathology Section of the Clinical Brain Disorders Branch of the National Institute of Mental Health.

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