Comparability of Different Methods for Estimating Influenza Infection Rates Over a Single Epidemic Wave

Abstract

Estimation of influenza infection rates is important for determination of the extent of epidemic spread and for calculation of severity indicators. The authors compared estimated infection rates from paired and cross-sectional serologic surveys, rates of influenza like illness (ILI) obtained from sentinel general practitioners (GPs), and ILI samples that tested positive for influenza using data from similar periods collected during the 2009 H1N1 epidemic in Singapore. The authors performed sensitivity analyses to assess the robustness of estimates to input parameter uncertainties, and they determined sample sizes required for differing levels of precision. Estimates from paired seroconversion were 17% (95% Bayesian credible interval (BCI): 14, 20), higher than those from cross-sectional serology (12%, 95% BCI: 9, 17). Adjusted ILI estimates were 15% (95% BCI: 10, 25), and estimates computed from ILI and laboratory data were 12% (95% BCI: 8, 18). Serologic estimates were least sensitive to the risk of input parameter misspecification. ILI-based estimates were more sensitive to parameter misspecification, though this was lessened by incorporation of laboratory data. Obtaining a 5-percentage-point spread for the 95% confidence interval in infection rates would require more than 1,000 participants per serologic study, a sentinel network of 90 GPs, or 50 GPs when combined with laboratory samples. The various types of estimates will provide comparable findings if accurate input parameters can be obtained.

Severe Pediatric Adenovirus 7 Disease in Singapore Linked to Recent Outbreaks across Asia

“…All male citizens of Singapore undergo 2 years of conscripted military service upon turning 18-19 years of age; new personnel continuously enter the camps. To identify cases among the civilian population, the NPHL ILI laboratory surveillance network processes upper respiratory tract samples from patients with acute onset of fever (oral temperature ≥38°C) and respiratory symptoms referred by physicians at 23 sentinel clinics (6).…”

Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study

“…Furthermore, given the importance of high-risk groups in contributing to the overall burden of disease, modelling of the impact of vaccine-induced changes in transmission on burden of disease needs to take account of this additional population heterogeneity. Recently, models using Bayesian evidence synthesis have been developed to estimate the severity of influenza [11] and influenza infection attack rates [12],[13]. We combine similar Bayesian techniques with transmission models in a novel approach that provides evidence to inform vaccine policy decisions.…”

Interpreting Seroepidemiologic Studies of Influenza in a Context of Nonbracketing Sera

“…In some situations when pre-epidemic seroprevalence is very low, a cross-sectional study with only post-epidemic specimens can be used to estimate cumulative incidence [7]. The second type corresponds to longitudinal studies in which sera are collected from the same persons before and after an epidemic, and the cumulative incidence of infection is estimated by the proportion of persons with 4-fold or greater rises in antibody titers in paired specimens [3,8]. Smaller rises are traditionally ignored because of the potential for assay variability and measurement error [9–11].…”