Inferring Influenza Infection Attack Rate from Seroprevalence Data

Abstract

Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN_1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3–12, 13–19, 20–29, 30–59 became MN_1∶40 seropositive, which was much lower than the 90%–100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks. Seroprevalence studies have been regarded as the most practical method for accurately estimating the number of infections in influenza epidemics and pandemics. However, methods for inferring the number of infections from seroprevalence data in previous studies have mostly been based on conventional practice instead of standardized criteria. Specifically, there are no systematic criteria on how to select the seropositivity threshold and adjust for the proportion of infections that become seropositive. Here, we showed that under the conventional criteria, the number of 2009 pandemic influenza A/H1N1 infections had been substantially underestimated in Hong Kong as well as other countries, mostly due to overestimation of the proportion of infections that became seropositive. Our results highlighted the need to reexamine the widely accepted practice in interpreting seroprevalence data, especially in the context of pandemics when little is known but robust and comparable estimates of the number of infections and severity are most needed for informing situational awareness and guiding control policies.