Using Auxiliary Information to Improve Wildlife Disease Surveillance When Infected Animals Are Not Detected: A Bayesian Approach

<div><p>There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows “apples-to-apples” comparisons of surveillance efficiencies between units where heterogeneous samples were collected.</p></div

Nominal weights as a function of the prevalence ratio and prevalence <i>π₀</i>.

<p>Nominal weights are shown for 5 fixed prevalence ratios: 10, 5, 2, 1, and 0.5, which are in ascending order in the figure. The x-axis is the denominator prevalence <i>π₀</i>. Nominal weights increase rapidly as the numerator prevalence <i>π₁</i> approaches 1; as the numerator class becomes more like a “perfect sentinel”.</p

Factors controlling the departure of real and nominal weights.

<p>The red curves correspond to a prevalence ratio of 10, and the black curves correspond to a prevalence ratio of 2. For each fixed prevalence ratio, two sample sizes (plotting symbol  = 1) and (plotting symbol  = 2) are shown. For a fixed prevalence ratio and sample size, one can vary the number of positives in class 0 (<i>C₀</i>), and compute the corresponding number of positives in class 1 (<i>C₁</i>). The x-axis is <i>C₀</i>. One can then compute the nominal and real weights from <i>C₀</i>, <i>C₁</i>, <i>N₀</i>, and <i>N₁</i>. The primary determinate for departures between the real and nominal weights appears to be the number of positives in the sample (x-axis), and not the total sample size (1 versus 2 plotting symbol). The apparent prevalence ratio (red versus black) appears to play a minor secondary role.</p

Estimates of nominal CWD surveillance weights for 8 classes of mule deer from Colorado (data from WM[15]) using a binomial complementary log-log regression model with Bayesian and maximum likelihood approaches, as well as a Poisson regression model.

<p><i>Notes</i>: The <i>Harvest-adult-M</i> category is used as the reference class in these analyses, as in WM <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089843#pone.0089843-Walsh1" target="_blank">[15]</a>. We provide both the count of CWD positive animals (<i>C</i>) and the total number sampled (<i>N</i>) from WM <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089843#pone.0089843-Walsh1" target="_blank">[15]</a>.</p

Nominal and real surveillance weights calculated using data from WM[15].

<p>For real weights, a sample equivalent to reference class animals was needed to obtain the target goal, which is for the posterior probability .</p><p><i>Notes</i>: Values for nominal weights are the Bayesian posterior means of the hazard ratios. Real weights were obtained by posterior credible bound matching, described in the text.</p

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Part 2 - data for multinomial partitioning when an end point occurs with prior prediction probabilities to account for uncertainty, data are pipe delimited

Systematic Study of Chromatographic Behavior vs Alkyl Chain Length for HPLC Bonded Phases Containing an Embedded Carbamate Group

A series of HPLC bonded phases containing an internal carbamate group were studied by changing the terminal N-alkyl group from C8H17 to C18H37 in increments of two methylene units, i.e., C8, C10, C12, C14, C16, and C18. Each material was prepared via bonding of silica with the respective 3-(chlorodimethylsilyl)propyl N-alkylcarbamate silane. The materials were compared under reversed-phase conditions using a test mixture of nonpolar, polar, and basic compounds in a 65:35 (v/v) methanol/20 mM KH2PO4/K2HPO4, pH 7, mobile phase. Retention factors were found to generally increase from the C8 length to the C12−C16 lengths but decreased for the C18 length. Retention factors were also measured as a function of three ligand surface concentrations for the C12, C14, and C18 materials. In this study, retention generally decreased with increasing surface concentration, especially for the C18 chain length. Changes in particle surface area and porosity caused by bonding did not fully account for the observed changes in retention factors. Peak shapes for the basic analytes propranolol and amitriptyline were also studied as a function of N-alkylcarbamate chain length and surface concentration. Tailing factors were unaffected by chain length and only weakly dependent on surface concentration. By comparison, tailing factors decreased significantly as surface concentration increased for a set of conventional C18 alkyl packings

Synthesis and Surface Chemistry of Spherical Mesoporous Organic−Inorganic Hybrid Particles with an Integrated Alcohol Functionality on the Pore Surface

Novel mesoporous organic−inorganic spherical hybrid particles are described that contain a 3-hydroxypropyl organic functionality which is integral to the pore surface. The 3-hydroxypropyl hybrid particle is synthesized in three steps starting from a 4:1 (mol/mol) mixture of tetraethoxysilane and [3-(methacryloxy)propyl]trimethoxysilane, where the monomers are polymerized to a poly(organoalkoxysilane) oil, followed by sol−gel reaction to the hybrid silicate bead, which is finally subjected to an alkaline hydrothermal treatment to liberate the alcohol from the ester protecting group. The silicate precursor and final product were characterized by NMR spectroscopy and nitrogen sorption analysis. The heterogeneous surface chemistry of the hybrid's alcohol functionality was explored by running a series of classical alcohol reactions including bromination, esterification (carbamic and carbonic), and etherification (Williamson, epoxide ring opening). The brominated analogue was further converted via cyanation and Grignard couplings. Nuances to the heterogeneous surface chemistry are discussed as well as product characterizations by NMR spectroscopy and combustion analysis. A stability study was further conducted on the 3-hydroxypropyl hybrid silicate using an alkaline resistance test under HPLC packed column conditions. The hybrid material was found to be over 10-fold more stable than a comparable silica gel material. In a second HPLC test, the cyano derivatized hybrid material was found to be more resistant to acid-induced siloxane cleavage vs a comparable (3-cyanopropyl)silane grafted silica gel

Venn diagrams of cross-community authorship through time.

Each year’s Venn diagram is scaled to reflect the number of authors with two or more papers in our paper bank over the preceding 5 y. Number of authors with two papers in the same journal community are represented by disjointed regions of the circles, and number of authors with papers in two different communities are represented by the area of the intersections. Each circle is scaled to reflect the total number of authors with papers in that community during the 5 y prior to the label year. Areas are on a log-scale, and total number of authors with multiple papers each year is reported below each Venn diagram. Data to generate this figure are contained in S1 Data.</p

Cross-disciplinary citations through time.

<p>(A) Citations from papers in ecology journals to papers in each journal community. (B) Citations from papers in veterinary journals to papers in each journal community. (C) Citations from papers in Group 3 journals to papers in each journal community. Shaded regions are 95% confidence intervals from a Poisson generalized additive model fit to each journal community's time series. Data to generate this figure are contained in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002448#pbio.1002448.s001" target="_blank">S1 Data</a>.</p