Abstract
Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI: 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI: 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs.
Highlights
Each year, seasonal influenza is responsible for about three to five millions severe illnesses and about 250,000 to 500,000 deaths worldwide [1]
The proportion of individuals experiencing a 2-fold rise ranged from 20% to 33% with lower bounds of the 95% CIs above 0, indicating that these rises cannot be solely explained by measurement errors
Assuming that most of these 2-fold rises were due to infection, our estimate of infection attack rates AR§2f :r: for H1N1 in 2008 and H1N1, H3N2 and B in Spring 2009 would be dramatically higher than traditional estimate AR§4f :r: based on 4-fold rises or more (Figure 3A)
Summary
Seasonal influenza is responsible for about three to five millions severe illnesses and about 250,000 to 500,000 deaths worldwide [1]. These epidemics can generate important economic losses due to high levels of worker absenteeism as well as a saturation of emergency services at the peak of the epidemic [1]. Appropriate assessment of the epidemiological characteristics of the influenza virus is important to guide control policies This requires being able to track the number of influenza cases with severe clinical outcomes (i.e. the tip of the severity pyramid) as well as the total number of people infected by an influenza virus (i.e. the base of the severity pyramid). Estimates of infection attack rates are essential for characterizing the spread of the virus in human populations in order to predict epidemic trajectory, the potential impact of control measures such as social distancing measures, and the likelihood and magnitude of subsequent epidemics arising from continued circulation of the same virus [2,3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
