Geographic Biases in Human Lyme disease Surveillance and Utility of Companion Animal Sentinel Programs: Exploratory Spatial Analysis

Background and Objectives: Mapping and exploratory spatial data analyses are ideal tools for characterizing spread and occurrence of human Lyme disease infection. Unfortunately, many mapped displays utilizing Lyme disease surveillance data are prone to bias due to a lack of consideration for geographical confounders. The objectives of our study were to 1) characterize the geographic effects that boundary and travel related biases have on visualization of human Lyme disease occurrence and 2) apply these findings to develop a more precise methodology for evaluating efficacy of animal sentinel surveillance programs in predicting incidence of human Lyme disease infection.;Methods: County-level human Lyme disease and companion animal tick surveillance data were obtained from relevant state health departments. Data were organized within Microsoft Excel spreadsheets, and sorted by relevant reporting year and county. In Study 1, boundary effects were evaluated for the region containing Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia 2010-2014, utilizing a combination of rate smoothing and local indicators of spatial autocorrelation. Trends in disease clustering over time within our multistate region were evaluated utilizing logistic generalized estimating equations. In Study 2, travel associated biases were evaluated only for West Virginia confirmed Lyme disease cases 2011-2015, utilizing a combination of paired t-test, Wilcoxon Rank Signed test, and local indicators of spatial autocorrelation. In Study 3, the efficacy of the companion animal (dog and cat) sentinel surveillance program in West Virginia 2014-2016, was evaluated utilizing a combination of ordinary least squares and spatial regression techniques as well as local indicators of spatial autocorrelation on regression residuals.;Results: Study 1. Analyses indicated statistically significant ( P = 0.05) clustering of human Lyme disease incidence over time. High-high clusters aggregated near counties bordering high incidence states, while low-low clusters aggregated near shared county borders in non-high incidence states. Study 2. Analyses indicated statistical non-equivalency using paired t-test (t = 3.99, df = 54, P = 0.0002) and the non-parametric Wilcoxon Signed Rank test (S=264, P \u3c 0.001) between total overall cases and those obtained within patient\u27s home county, suggesting significant travel-associated bias. Additionally, local indicators of spatial autocorrelation detected statistically significant ( P = 0.05) patterns of clustering in the county level proportion of cases attributable to travel. Study 3. Regression analyses identified significant associations between confirmed cases of human Lyme disease and average number of Ixodes scapularis removed from dogs (ordinary least squares (beta=0.20 P \u3c 0.001) and spatial lag (beta = 0.12, P = 0.002) models) but not cats for the period 2014-2016. Local indicators of spatial autocorrelation produced for spatial lag regression residuals indicated a decrease in model over and underestimation, but identified a higher number of statistically significant outliers than ordinary least squares regression.;Conclusions: Results of spatial and regression analyses 1) indicate significant differential clustering of incident human Lyme disease within WV and surrounding states over time; 2) suggest substantial travel-associated bias in Lyme disease case visualization within WV; and 3) strongly support the use of companion animal, and specially dog sentinel surveillance programs for estimation of human Lyme disease risk within WV. These findings suggest that geographic biases significantly affect visualization of human Lyme disease incidence and support the effectiveness of utilizing dogs as sentinel populations to estimate human risk. Findings of these three studies highlight the importance of using statistical methodologies that can accommodate the spatial structure imbedded within public health surveillance data

Identification and Characterization of Peak Activity, Environmental Variables, and Bacterial Pathogens in A. americanum L. at Ames Plantation, West Tennessee

The status of tick-borne diseases (TBD) in the southeastern United States is uncertain due to a number of factors including, but not limited to emerging pathogens, misdiagnoses, and modifications to landscapes. Ehrlichiosis and rickettiosis are two of the most common TBDs; these are caused by Ehrlichia and Rickettsia bacteria that can be transmitted by a number of different tick species. The objectives of this study were to identify Amblyomma americanum (the Lone Star tick) peak activity and habitat preferences and characterize the potential role of A. americanum in tick-borne disease cycles in southwestern Tennessee. Using vegetation drags and CO2-baited traps, ticks were collected monthly from May to September 2012 from 100 sites on the Ames Plantation Research and Education Center (Ames). Using a one-way analysis of variance, we identified the peak activity of A. americanum for adults as being in May or June and of nymphs as being bimodal with a peak in June and again in August. Trapping data were analyzed in a contingency table; results indicated significant trapping differences in the number of nymphs and adults collected by the two trapping methods. Environmental and trapping data were correlated using an ANCOVA to evaluate trapping efficacy under different environmental stressors and to identify landscapes in which A. americanum adults and nymphs are notably more abundant. Of 925 adult A. americanum screened for Ehrlichia and Rickettsia bacteria, 1.8% (n = 17) and 38% (n = 353) were PCR positive, of which 8 ticks (0.8%) were positive with both pathogens. Using ArcGIS we displayed pathogen positive A. americanum locations; calculating Moran’s I for each pathogen indicated there was no significant clustering among pathogen positive locations. The identification of pathogens and co-infections within A. americanum from western Tennessee warrants further investigations to understand the role ticks and their environment have in the distribution of TBD

METHOD FOR SWAPPING OUT USER-GENERATED PHOTOS WITH HIGH QUALITY PHOTOS

The present application discloses a system and method to assist a photographer to swap out their user-generated photo with a high quality photo by using appropriate software. The disclosed system includes a software application that enables the user to upload a captured photo for processing, which may include the user’s image. A landscape or object in the photo is detected by the application, which searches for a high quality photo of the same object, matching the user-generated photo. The application then swaps the user’s image into the high quality photo and/or allows the user to customize it, before cropping and aligning the image to match the user captured photo. The method reduces time and eliminates manual effort for improving the quality of the captured photo

Test Population Selection from Weibull-Based, Monte Carlo Simulations of Fatigue Life

Fatigue life is probabilistic and not deterministic. Experimentally establishing the fatigue life of materials, components, and systems is both time consuming and costly. As a result, conclusions regarding fatigue life are often inferred from a statistically insufficient number of physical tests. A proposed methodology for comparing life results as a function of variability due to Weibull parameters, variability between successive trials, and variability due to size of the experimental population is presented. Using Monte Carlo simulation of randomly selected lives from a large Weibull distribution, the variation in the L10 fatigue life of aluminum alloy AL6061 rotating rod fatigue tests was determined as a function of population size. These results were compared to the L10 fatigue lives of small (10 each) populations from AL2024, AL7075 and AL6061. For aluminum alloy AL6061, a simple algebraic relationship was established for the upper and lower L10 fatigue life limits as a function of the number of specimens failed. For most engineering applications where less than 30 percent variability can be tolerated in the maximum and minimum values, at least 30 to 35 test samples are necessary. The variability of test results based on small sample sizes can be greater than actual differences, if any, that exists between materials and can result in erroneous conclusions. The fatigue life of AL2024 is statistically longer than AL6061 and AL7075. However, there is no statistical difference between the fatigue lives of AL6061 and AL7075 even though AL7075 had a fatigue life 30 percent greater than AL6061

Brief report: Cause of death among people discharged from infective endocarditis related hospitalization—West Virginia, 2016–2019

Background and Objectives Compare proportion of all-cause and cause-specific mortality among West Virginia Medicaid enrollees who were discharged from infective endocarditis (IE) hospitalization with and without opioid use disorder (OUD) diagnosis. Methods The proportions of cause-specific deaths among those who were discharged from IE-related hospitalizations were compared by OUD diagnosis. Results The top three underlying causes of death discharged from IE hospitalization were accidental drug poisoning, mental and behavioral disorders due to polysubstance use, and cardiovascular diseases. Of the total deaths occurring among patients discharged after IE-related hospitalization, the proportion has increased seven times from 2016 to 2019 among the OUD deaths while it doubled among the non-OUD deaths. Discussion and Conclusions Of the total deaths occurring among patients discharged after IE-related hospitalization, the increase is higher in those with OUD diagnosis. OUD is becoming a significantly negative impactor on the survival outcome among IE patients. It is of growing importance to deliver medication for OUD treatment and harm reduction efforts to IE patients in a timely manner, especially as the COVID-19 pandemic persists

Probabilistic Analysis for Comparing Fatigue Data Based on Johnson-Weibull Parameters

Leonard Johnson published a methodology for establishing the confidence that two populations of data are different. Johnson's methodology is dependent on limited combinations of test parameters (Weibull slope, mean life ratio, and degrees of freedom) and a set of complex mathematical equations. In this report, a simplified algebraic equation for confidence numbers is derived based on the original work of Johnson. The confidence numbers calculated with this equation are compared to those obtained graphically by Johnson. Using the ratios of mean life, the resultant values of confidence numbers at the 99 percent level deviate less than 1 percent from those of Johnson. At a 90 percent confidence level, the calculated values differ between +2 and 4 percent. The simplified equation is used to rank the experimental lives of three aluminum alloys (AL 2024, AL 6061, and AL 7075), each tested at three stress levels in rotating beam fatigue, analyzed using the Johnson- Weibull method, and compared to the ASTM Standard (E739 91) method of comparison. The ASTM Standard did not statistically distinguish between AL 6061 and AL 7075. However, it is possible to rank the fatigue lives of different materials with a reasonable degree of statistical certainty based on combined confidence numbers using the Johnson- Weibull analysis. AL 2024 was found to have the longest fatigue life, followed by AL 7075, and then AL 6061. The ASTM Standard and the Johnson-Weibull analysis result in the same stress-life exponent p for each of the three aluminum alloys at the median, or L(sub 50), live