Bandit Models of Human Behavior: Reward Processing in Mental Disorders

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. We demonstrate empirically that the proposed parametric approach can often outperform the baseline Thompson Sampling on a variety of datasets. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions.Comment: Conference on Artificial General Intelligence, AGI-1

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.Influenza activity data from 2003-04 through 2007-08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003-04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003-04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior

Validation of Statistical Models for Estimating Hospitalization Associated with Influenza and Other Respiratory Viruses

BACKGROUND: Reliable estimates of disease burden associated with respiratory viruses are keys to deployment of preventive strategies such as vaccination and resource allocation. Such estimates are particularly needed in tropical and subtropical regions where some methods commonly used in temperate regions are not applicable. While a number of alternative approaches to assess the influenza associated disease burden have been recently reported, none of these models have been validated with virologically confirmed data. Even fewer methods have been developed for other common respiratory viruses such as respiratory syncytial virus (RSV), parainfluenza and adenovirus. METHODS AND FINDINGS: We had recently conducted a prospective population-based study of virologically confirmed hospitalization for acute respiratory illnesses in persons <18 years residing in Hong Kong Island. Here we used this dataset to validate two commonly used models for estimation of influenza disease burden, namely the rate difference model and Poisson regression model, and also explored the applicability of these models to estimate the disease burden of other respiratory viruses. The Poisson regression models with different link functions all yielded estimates well correlated with the virologically confirmed influenza associated hospitalization, especially in children older than two years. The disease burden estimates for RSV, parainfluenza and adenovirus were less reliable with wide confidence intervals. The rate difference model was not applicable to RSV, parainfluenza and adenovirus and grossly underestimated the true burden of influenza associated hospitalization. CONCLUSION: The Poisson regression model generally produced satisfactory estimates in calculating the disease burden of respiratory viruses in a subtropical region such as Hong Kong

Safety, efficacy, and immunogenicity of an inactivated influenza vaccine in healthy adults: a randomized, placebo-controlled trial over two influenza seasons

<p>Abstract</p> <p>Background</p> <p>Seasonal influenza imposes a substantial personal morbidity and societal cost burden. Vaccination is the major strategy for influenza prevention; however, because antigenically drifted influenza A and B viruses circulate annually, influenza vaccines must be updated to provide protection against the predicted prevalent strains for the next influenza season. The aim of this study was to assess the efficacy, safety, reactogenicity, and immunogenicity of a trivalent inactivated split virion influenza vaccine (TIV) in healthy adults over two influenza seasons in the US.</p> <p>Methods</p> <p>The primary endpoint of this double-blind, randomized study was the average efficacy of TIV versus placebo for the prevention of vaccine-matched, culture-confirmed influenza (VMCCI) across the 2005-2006 and 2006-2007 influenza seasons. Secondary endpoints included the prevention of laboratory-confirmed (defined by culture and/or serology) influenza, as well as safety, reactogenicity, immunogenicity, and consistency between three consecutive vaccine lots. Participants were assessed actively during both influenza seasons, and nasopharyngeal swabs were collected for viral culture from individuals with influenza-like illness. Blood specimens were obtained for serology one month after vaccination and at the end of each influenza season's surveillance period.</p> <p>Results</p> <p>Although the point estimate for efficacy in the prevention of all laboratory-confirmed influenza was 63.2% (97.5% confidence interval [CI] lower bound of 48.2%), the point estimate for the primary endpoint, efficacy of TIV against VMCCI across both influenza seasons, was 46.3% with a 97.5% CI lower bound of 9.8%. This did not satisfy the pre-specified success criterion of a one-sided 97.5% CI lower bound of >35% for vaccine efficacy. The VMCCI attack rates were very low overall at 0.6% and 1.2% in the TIV and placebo groups, respectively. Apart from a mismatch for influenza B virus lineage in 2005-2006, there was a good match between TIV and the circulating strains. TIV was highly immunogenic, and immune responses were consistent between three different TIV lots. The most common reactogenicity events and spontaneous adverse events were associated with the injection site, and were mild in severity.</p> <p>Conclusions</p> <p>Despite a good immune response, and an average efficacy over two influenza seasons against laboratory-confirmed influenza of 63.2%, the pre-specified target (lower one-sided 97.5% confidence bound for efficacy > 35%) for the primary efficacy endpoint, the prevention of VMCCI, was not met. However, the results should be interpreted with caution in view of the very low attack rates we observed at the study sites in the 2005-2006 and 2006-2007, which corresponded to relatively mild influenza seasons in the US. Overall, the results showed that TIV has an acceptable safety profile and offered clinical benefit that exceeded risk.</p> <p>Trial registration</p> <p>NCT00216242</p

Comparison of the novel ResPlex III assay and existing techniques for the detection and subtyping of influenza virus during the influenza season 2006–2007

Influenza virus is a major cause of disease worldwide. The accurate detection and further subtyping of influenza A viruses are important for epidemiologic surveillance, and subsequent comprehensive characterization of circulating influenza viruses is essential for the selection of an optimal vaccine composition. ResPlex III is a new multiplex reverse transcriptase polymerase chain reaction (RT-PCR)-based method for detecting, typing, and subtyping influenza virus in clinical specimens. The ResPlex III assay was compared with other methods with respect to sensitivity and accuracy, using 450 clinical specimens obtained from subjects throughout Germany during the 2006–2007 influenza season. Samples were analyzed for the presence of influenza virus in Madin-Darby canine kidney (MDCK) cells by rapid cell culture using peroxidase staining and conventional cell culture confirmed by hemagglutination inhibition assay, a rapid diagnostic assay (Directigen Flu A+B test; BD Diagnostic Systems, Heidelberg, Germany), in-house real-time RT-PCR (RRT-PCR), and ResPlex III (Qiagen, Hilden, Germany). ResPlex III had the highest sensitivity for detecting influenza virus in clinical specimens, followed by in-house RRT-PCR (96% compared with ResPlex III). Conventional cell culture in MDCK cells, rapid culture, and quick test assays were substantially less sensitive (55%, 72%, and 39%, respectively). Virus subtyping results were identical using ResPlex III and the standard virological subtyping method, hemagglutination inhibition. ResPlex III is a quick, accurate, and sensitive assay for detecting and typing influenza A and B viruses and subtyping influenza A viruses in clinical specimens, and might be considered for a supplemental role in worldwide seasonal and pandemic influenza surveillance

Statistical estimates of absenteeism attributable to seasonal and pandemic influenza from the Canadian Labour Force Survey

<p>Abstract</p> <p>Background</p> <p>As many respiratory viruses are responsible for influenza like symptoms, accurate measures of the disease burden are not available and estimates are generally based on statistical methods. The objective of this study was to estimate absenteeism rates and hours lost due to seasonal influenza and compare these estimates with estimates of absenteeism attributable to the two H1N1 pandemic waves that occurred in 2009.</p> <p>Methods</p> <p>Key absenteeism variables were extracted from Statistics Canada's monthly labour force survey (LFS). Absenteeism and the proportion of hours lost due to own illness or disability were modelled as a function of trend, seasonality and proxy variables for influenza activity from 1998 to 2009.</p> <p>Results</p> <p>Hours lost due to the H1N1/09 pandemic strain were elevated compared to seasonal influenza, accounting for a loss of 0.2% of potential hours worked annually. In comparison, an estimated 0.08% of hours worked annually were lost due to seasonal influenza illnesses. Absenteeism rates due to influenza were estimated at 12% per year for seasonal influenza over the 1997/98 to 2008/09 seasons, and 13% for the two H1N1/09 pandemic waves. Employees who took time off due to a seasonal influenza infection took an average of 14 hours off. For the pandemic strain, the average absence was 25 hours.</p> <p>Conclusions</p> <p>This study confirms that absenteeism due to seasonal influenza has typically ranged from 5% to 20%, with higher rates associated with multiple circulating strains. Absenteeism rates for the 2009 pandemic were similar to those occurring for seasonal influenza. Employees took more time off due to the pandemic strain than was typical for seasonal influenza.</p

Model Selection in Time Series Studies of Influenza-Associated Mortality

Background: Poisson regression modeling has been widely used to estimate influenza-associated disease burden, as it has the advantage of adjusting for multiple seasonal confounders. However, few studies have discussed how to judge the adequacy of confounding adjustment. This study aims to compare the performance of commonly adopted model selection criteria in terms of providing a reliable and valid estimate for the health impact of influenza. Methods: We assessed four model selection criteria: quasi Akaike information criterion (QAIC), quasi Bayesian information criterion (QBIC), partial autocorrelation functions of residuals (PACF), and generalized cross-validation (GCV), by separately applying them to select the Poisson model best fitted to the mortality datasets that were simulated under the different assumptions of seasonal confounding. The performance of these criteria was evaluated by the bias and root-mean-square error (RMSE) of estimates from the pre-determined coefficients of influenza proxy variable. These four criteria were subsequently applied to an empirical hospitalization dataset to confirm the findings of simulation study. Results: GCV consistently provided smaller biases and RMSEs for the influenza coefficient estimates than QAIC, QBIC and PACF, under the different simulation scenarios. Sensitivity analysis of different pre-determined influenza coefficients, study periods and lag weeks showed that GCV consistently outperformed the other criteria. Similar results were found in applying these selection criteria to estimate influenza-associated hospitalization. Conclusions: GCV criterion is recommended for selection of Poisson models to estimate influenza-associated mortality and morbidity burden with proper adjustment for confounding. These findings shall help standardize the Poisson modeling approach for influenza disease burden studies. © 2012 Wang et al.published_or_final_versio