title: Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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%.
  
  
  CONCLUSIONS
  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.
authors: Department of Community Medicine and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China. joewu@hku.hk
authors: Joseph T
authors: JT
authors: Wu
authors: Andrew
authors: A
authors: Ho
authors: Edward S K
authors: ES
authors: Ma
authors: Cheuk Kwong
authors: CK
authors: Lee
authors: Daniel K W
authors: DK
authors: Chu
authors: Po-Lai
authors: PL
authors: Ho
authors: Ivan F N
authors: IF
authors: Hung
authors: Lai Ming
authors: LM
authors: Ho
authors: Che Kit
authors: CK
authors: Lin
authors: Thomas
authors: T
authors: Tsang
authors: Su-Vui
authors: SV
authors: Lo
authors: Yu-Lung
authors: YL
authors: Lau
authors: Gabriel M
authors: GM
authors: Leung
authors: Benjamin J
authors: BJ
authors: Cowling
authors: J S Malik
authors: JS
authors: Peiris
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D003430:Cross-Sectional Studies; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology* / Q000401:mortality; D008875:Middle Aged; D058873:Pandemics; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health; D016036:Seroepidemiologic Studies