Abstract
In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity. We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1-2 wk before, and 3 wk after epidemic peak for individuals aged 5-14 y, 15-29 y, and 30-59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5-14 y, 15-19 y, and 20-29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30-59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%-10%. Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered.
Highlights
One of the lessons learned from the 2009 H1N1 influenza pandemic was the need for rapid and reliable estimates of transmissibility and severity of the novel virus [1]
A companion study used paired sera collected from a cohort (1) to estimate the infection attack rate (IAR) and severity profile of pandemic A/H1N1 2009 (pdmH1N1) in Hong Kong and (2) to show that specimens collected around the peak of an epidemic from larger cohorts could have yielded more reliable severity estimates [8]
Seroprevalence, infection-hospitalization probability (IHP), and Final IAR in the Full Model The age-specific seroprevalence curves in the full model provided a reasonably good fit to the serial cross-sectional serologic data (Figure 3) except for the first 2 wk of September for 5–14 y olds
Summary
One of the lessons learned from the 2009 H1N1 influenza (pdmH1N1) pandemic was the need for rapid and reliable estimates of transmissibility and severity (the probability of severe outcomes, such as hospitalization and death, if infected) of the novel virus [1]. This is crucial for public health planning and for effective communication with the public. Millions of people catch influenza—a viral infection of the airways—and about half a million die as a result These seasonal epidemics occur because small but frequent changes in the influenza virus mean that the immune response produced by infection with one year’s virus provides only partial protection against the year’s virus. By the time WHO announced that the pandemic was over (10 August 2010), pdmH1N1 had killed more than 18,000 people
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