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Background Influenza infection is often measured by a fourfold antibody titer increase over an influenza season (ie seroconversion). However, this approach may fail when influenza seasons are less distinct as it does not account for transient effects from recent infections. Here, we present a method to determine seroconversion for non‐paired sera, adjusting for changes in individuals’ antibody titers to influenza due to the transient impact of recent exposures, varied sampling times, and laboratory processes. Methods We applied our method using data for five H3N2 strains collected from 942 individuals, aged 2‐90 years, during the first two study visits of the Fluscape cohort study (2009‐2012) in Guangzhou, China. Results After adjustment, apparent seroconversion rates for non‐circulating strains decreased while we observed a 20% increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain (A/Brisbane/20/2007) in our study, participants aged under 18, and over 64 had the highest seroconversion rates compared to other age groups. Conclusions Our results highlight the need for improved methods when using antibody titers as an endpoint in settings where there is no clear influenza “off” season. Methods, like those presented here, that use titers from circulating and non‐circulating strains may be key.

Regularization, or shrinkage estimation, refers to a class of statistical methods that constrain the variability of parameter estimates when fitting models to data. These constraints move parameters toward a group mean or toward a fixed point (e.g., 0). Regularization has gained popularity across many fields for its ability to increase predictive power over classical techniques. However, articles published in JEAB and other behavioral journals have yet to adopt these methods. This paper reviews some common regularization schemes and speculates as to why articles published in JEAB do not use them. In response, we propose our own shrinkage estimator that avoids some of the possible objections associated with the reviewed regularization methods. Our estimator works by mixing weighted individual and group (WIG) data rather than by constraining parameters. We test this method on a problem of model selection. Specifically, we conduct a simulation study on the selection of matching-law-based punishment models, comparing WIG with ordinary least squares (OLS) regression, and find that, on average, WIG outperforms OLS in this context.

A direct-suppression, or subtractive, model of punishment has been supported as the qualitatively and quantitatively superior matching law-based punishment model (Critchfield, Paletz, MacAleese, & Newland, 2003; de Villiers, 1980; Farley, 1980). However, this conclusion was made without testing the model against its predecessors, including the original (Herrnstein, 1961) and generalized (Baum, 1974) matching laws, which have different numbers of parameters. To rectify this issue, we reanalyzed a set of data collected by Critchfield et al. (2003) using information theoretic model selection criteria. We found that the most advanced version of the direct-suppression model (Critchfield et al., 2003) does not convincingly outperform the generalized matching law, an account that does not include punishment rates in its prediction of behavior allocation. We hypothesize that this failure to outperform the generalized matching law is due to significant theoretical shortcomings in model development. To address these shortcomings, we present a list of requirements that all punishment models should satisfy. The requirements include formal statements of flexibility, efficiency, and adherence to theory. We compare all past punishment models to the items on this list through algebraic arguments and model selection criteria. None of the models presented in the literature thus far meets all of the requirements.

BACKGROUND: Serum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses. METHODS: 151 study participants from near Guangzhou, China were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results. RESULTS: We observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, rho=0.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (>80%) for predicting NT with HI. CONCLUSIONS: While we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, though attention should be given to the effect of age on these assays. This article is protected by copyright. All rights reserved.

CONSISE - The consortium for the Standardization of Influenza Seroepidemiology - is a global partnership to develop influenza investigation protocols and standardize seroepidemiology to inform health policy. This international partnership was formed in 2011 and was created out of a need, identified during the 2009 H1N1 pandemic, for timely seroepidemiological data to better estimate pandemic virus infection severity and attack rates to inform policy decisions. CONSISE has developed into a consortium of two interactive working groups: epidemiology and laboratory, with a steering committee composed of individuals from several organizations. CONSISE has had two international meetings with more planned for 2013. We seek additional members from public health agencies, academic institutions and other interested parties.

Please cite this paper as: Laurie et al. (2013) Influenza serological studies to inform public health action: best practices to optimise timing, quality and reporting. Influenza and Other Respiratory Viruses 7(2) 211–224. Background Serological studies can detect infection with a novel influenza virus in the absence of symptoms or positive virology, providing useful information on infection that goes beyond the estimates from epidemiological, clinical and virological data. During the 2009 A(H1N1) pandemic, an impressive number of detailed serological studies were performed, yet the majority of serological data were available only after the first wave of infection. This limited the ability to estimate the transmissibility and severity of this novel infection, and the variability in methodology and reporting limited the ability to compare and combine the serological data. Objectives To identify best practices for conduct and standardisation of serological studies on outbreak and pandemic influenza to inform public policy. Methods/Setting An international meeting was held in February 2011 in Ottawa, Canada, to foster the consensus for greater standardisation of influenza serological studies. Results Best practices for serological investigations of influenza epidemiology include the following: classification of studies as pre-pandemic, outbreak, pandemic or inter-pandemic with a clearly identified objective; use of international serum standards for laboratory assays; cohort and cross-sectional study designs with common standards for data collection; use of serum banks to improve sampling capacity; and potential for linkage of serological, clinical and epidemiological data. Advance planning for outbreak studies would enable a rapid and coordinated response; inclusion of serological studies in pandemic plans should be considered. Conclusions Optimising the quality, comparability and combinability of influenza serological studies will provide important data upon emergence of a novel or variant influenza virus to inform public health action.

Please cite this paper as: Pepin et al. (2012) Multiannual patterns of influenza A transmission in Chinese live bird market systems. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750-2659.2012.00354.x. Background Avian influenza viruses (AIV) cause huge economic losses in poultry industries and pose a substantial threat to human health. However, predicting AIV epizootics and emergence in humans is confounded by insufficient empirical data on the ecology and dynamics of AIV in poultry systems. To address this gap, we quantified incidence patterns for 13 hemagglutinin subtypes of AIV using 6 years of surveillance data that were collected from ten different species of poultry and three different types of poultry holdings (contexts) – retail, wholesale, or farms. Methods We collected 42 646 samples in Shantou, China between 2000 and 2006. We screened samples for hemagglutinin subtypes 1–13 of AIV and Avian Paramyxovirus-type-1 (APMV-1) using monospecific antisera in hemagglutination inhibition tests. We analyzed the data to determine seasonality patterns, subtype–host, and subtype–subtype interactions as well as subtype bias in incidence in different contexts. Results H3, H6, H9, and APMV-1 were the most prevalent. No significant seasonality was found when all subtypes were considered together. For most AIV subtypes and APMV-1, there was subtype specificity for host, context, and coinfection partner. H5 showed the most generalized host usage pattern, followed by H9 and H6. Conclusion Subtype-specific patterns because of host, context, and other subtypes suggest that risk assessments that exclude these details are likely inaccurate. Surveillance should include longitudinal sampling of multiple host species in multiple contexts. Quantitative models of control strategies must consider multiple subtypes, hosts, and source contexts to assess the effectiveness of interventions.

Data were collected from three sites on the waste rock dump at ERA Ranger Mine: (1) the cap site which was unvegetated and unripped with a surface slope of 2·8 per cent; (2) the batter site, surface slope 20·7 per cent, also unvegetated and unripped but with a covering of coarse rock material; and (3) the soil site, surface slope 1·2 per cent, which had c. 90 per cent vegetation cover of low shrubs and grasses and had been topsoiled and surface ripped. Natural rainfall events were monitored on the sites to collect rainfall, runoff and soil loss data to parameterize a sediment transport model of the form T = β S ∫ Q dt, and the DISTFW rainfall–runoff model. Low frequency, high intensity events resulted in the greatest soil loss. To accurately predict sediment loss during high intensity events, storms with a range of intensities were selected to derive the sediment transport model. DISTFW hydrology model parameters were derived by fitting four monitored events simultaneously. The selected parameters for the vegetated and ripped case may overpredict discharge for some rainfall events resulting in conservative design of erosion control features on rehabilitated landforms. Copyright 2000 © Environmental Research Institute of the Supervising Scientist, Commonwealth of Australia.

There is a need to assess the long-term stability of engineered landforms associated with the rehabilitation of Ranger Uranium Mine, Northern Territory, Australia, as it is a requirement that mill tailings must be contained for periods in excess of 1000 years. The geomorphic model, SIBERIA, is calibrated on hydrologic and erosion data collected by a combination of monitoring and rainfall simulation experiments on the waste rock dumps of Ranger. Preliminary analysis of Ranger's preferred above-grade option suggests that erosion of the order of 7 to 8 m will occur on the structure in a period of 1000 years. This depth of erosion may be sufficient to compromise the integrity of containment. It is shown that SIBERIA has significant advantages over steady-state erosion models. Suggestions are made for the design that will enhance the stability of the structure and extend the structural life of the containment. © 1998 John Wiley & Sons, Ltd.