Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so-called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners.

When screening for infectious diseases, group testing has proven to be a cost efficient alternative to individual level testing. Cost savings are realized by testing pools of individual specimens (eg, blood, urine, saliva, and so on) rather than by testing the specimens separately. However, a common concern that arises in group testing is the so‐called “dilution effect.” This occurs if the signal from a positive individual's specimen is diluted past an assay's threshold of detection when it is pooled with multiple negative specimens. In this article, we propose a new statistical framework for group testing data that merges estimation and case identification, which are often treated separately in the literature. Our approach considers analyzing continuous biomarker levels (eg, antibody levels, antigen concentrations, and so on) from pooled samples to estimate both a binary regression model for the probability of disease and the biomarker distributions for cases and controls. To increase case identification accuracy, we then show how estimates of the biomarker distributions can be used to select diagnostic thresholds on a pool‐by‐pool basis. Our proposals are evaluated through numerical studies and are illustrated using hepatitis B virus data collected on a prison population in Ireland.

The Cognitive Rigor Matrix is a tool that teachers can use to analyze and reflect on questions and tasks and, in the process, increase the level of rigor.

No abstract

Drive-by health monitoring (DBHM) is an indirect structural health monitoring strategy that leverages vehicle mounted sensors to detect, locate, and quantify bridge damage. Presently, there exists the need for a multilevel damage classification strategy that is reliable at moderately fast speeds, can quantify physical crack depths, is noise tolerant, can classify damage across the length of a bridge, and does not reference labeled or baseline data. This study presents a novel Bayesian estimation technique that leverages spike and slab prior specifications on an embedded simplified vehicle-bridge model to perform multilevel damage classification without referencing baseline or labeled data. A novel methodology is also proposed that maps crack ratios identified on the simplified model to physical levels of damage. The feasibility of the damage classification and mapping strategy is evaluated through analytical studies for a variety of damage states and operating conditions. Specifically, the classification and mapping of a 0.05 crack ratio is studied across different locations while considering varying levels of noise, vehicle velocities, number of experimental vehicle passes, and model errors. The success of the overall methodology, even in the presence of noise, indicates that the DBHM approach will likely be successful handling physical data. In particular, the feasibility studies demonstrate that the DBHM methodology is capable of leveraging noisy experimental data to reliably detect, locate, and quantify small levels of crack damage across the length of a bridge while the vehicle is traveling at velocities as high as 20.11 m/s.