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 MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1∶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.

Figure 1. Prepandemic seroprevalence and the epidemic curve of pdmH1N1 in Hong Kong.

A Age-stratified pre-pandemic MN titer distributions which were estimated from serum samples collected in June and early-July 2009. For samples collected after July 2009, we only tested whether they were MN_1∶20 and MN_1:40 seropositive because of logistical constraints. B Epidemic curves of pdmH1N1 in Hong Kong and Shenzhen. Estimated weekly numbers of lab-confirmed cases in Shenzhen were extracted from .

Figure 2. Age-specific ΔS ₄₀/ΔS ₂₀ during the first wave of pdmH1N1 in Hong Kong.

ΔS ₄₀ and ΔS ₂₀ at each cross-section were estimated using the method described in our previous work . If ISP₂₀ and ISP₄₀ (among all pdmH1N1 infections) were the same as the proportions of clinical cases that became MN_1:20 and MN_1∶40 seropositive (i.e. around 100% and 90%, respectively [23], [24]), ΔS ₄₀/ΔS ₂₀ should have remained close to 0.9–1 (the horizontal dashed line) throughout the first wave, which was not the case in reality as shown here.

Figure 3. Posterior distributions of parameter estimates.

Different colors correspond to different POLYMOD contact matrices. A Age-dependent parameters including IARs (first column), ISP ₄₀ (second), and age-specific susceptibility (third). B Other parameters including R(0), T_g, ISP ₂₀, reduction in within-age-group mixing due to school closure (π ₀, π ₁, π ₂), seed size, and scaling factor for FOI from Shenzhen (ε_SZ).

Figure 4. Comparison of the data and the fitted model.

The hospitalization and serial cross-sectional seroprevalence data are shown in blue (vertical bars indicate 95% confidence intervals). Posterior intervals of hospitalizations and seroprevalence in the fitted model are shown as heat shades in which darker colors represent higher probability densities (i.e. highest density in red and zero density in white).