Forecasting the new case detection rate of leprosy in four states of Brazil : a comparison of modelling approaches

Background Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins. Methods A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012–2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches. Results Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000. Conclusions All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated

Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches.

BACKGROUND: Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins. METHODS: A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012-2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches. RESULTS: Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000. CONCLUSIONS: All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/2/ajt15814_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/1/ajt15814.pd

Sparse Representations and Nonlinear Image Processing for Inverse Imaging Solutions

This work applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from a low-quality observed image. Sparse representations of images have drawn a considerable amount of interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. The standard sparse representation, however, does not consider the intrinsic geometric structure present in the data, thereby leading to sub-optimal results. Using the concept that a signal is block sparse in a given basis —i.e., the non-zero elements occur in clusters of varying sizes — we present a novel and efficient algorithm for learning a sparse representation of natural images, called graph regularized block sparse dictionary (GRBSD) learning. We apply the proposed method towards two image restoration applications: 1) single-Image super-resolution, where we propose a local regression model that uses learned dictionaries from the GRBSD algorithm for super-resolving a low-resolution image without any external training images, and 2) image inpainting, where we use GRBSD algorithm to learn a multiscale dictionary to generate visually plausible pixels to fill missing regions in an image. Experimental results validate the performance of the GRBSD learning algorithm for single-image super-resolution and image inpainting applications. The second problem addressed in this work involves image enhancement for detection and segmentation of objects in images. We exploit the concept that even though data from various imaging modalities have high dimensionality, the data is sufficiently well described using low-dimensional geometrical structures. To facilitate the extraction of objects having such structure, we have developed general structure enhancement methods that can be used to detect and segment various curvilinear structures in images across different applications. We use the proposed method to detect and segment objects of different size and shape in three applications: 1) segmentation of lamina cribrosa microstructure in the eye from second-harmonic generation microscopy images, 2) detection and segmentation of primary cilia in confocal microscopy images, and 3) detection and segmentation of vehicles in wide-area aerial imagery. Quantitative and qualitative results show that the proposed methods provide improved detection and segmentation accuracy and computational efficiency compared to other recent algorithms

How Exposure To Mass Incarceration Is Associated With The Wellbeing Of Individuals And Families

Mass incarceration in America has far-reaching effects on individuals and their families. Although a large body of evidence has described the effects of incarceration on specific physical and social outcomes, there is almost no research on its effects on broader wellbeing, a more holistic measure of lived experience that can more sensitively reflect social welfare and structural vulnerability. The goal of this thesis is to assess the association between exposure to the American system of mass incarceration and wellbeing. We examine individual-level exposure to the broader criminal legal system (CLS), including police stops, arrests, and incarceration, and family-level exposure to incarceration among immediate and extended family members. We conducted a secondary analysis using de-identified data from the 2018 Family History of Incarceration Survey (FamHIS), a nationally representative, cross-sectional study of incarceration among family members (N=2815) which also includes items on individual CLS exposure and wellbeing. Wellbeing was measured using the 100 Million Healthier Lives Adult Wellbeing Assessment, a set of validated items that assess thriving or suffering within five domains of wellbeing: physical health, mental health, social wellbeing, spiritual wellbeing, and overall life evaluation. We calculated trends in wellbeing by individual and family CLS involvement, and used logistic regression to compare wellbeing across levels of individual and family CLS involvement, controlling for confounding by age, gender, race, income, housing type, employment status, education, marital status, family size, history of addiction, and individual incarceration. We estimate that 40% of Americans have had any police contact or incarceration, and about 60% of Americans have had any immediate or extended family member incarcerated. Thirty one percent of Black Americans have ever been incarcerated, compared to 17.4% of White Americans (P \u3c 0.001). Twelve percent of Black Americans have had an immediate family member incarcerated for more than 10 years, compared to 1.4% of White Americans (P \u3c 0.001). Compared to individuals without any CLS exposure, any personal CLS exposure was associated with lower wellbeing in every domain. Exposure to police stop and frisk was associated with similarly low wellbeing compared to multiple exposures to incarceration. Compared to individuals without any family incarceration exposure, any family incarceration was associated with lower wellbeing in every domain. Having increasing numbers of immediate family members incarcerated was associated with progressively lower wellbeing in every domain (P \u3c 0.05 for each trend). Taken together, our findings show that any individual CLS exposure or family member exposure to incarceration is associated with decreased wellbeing in every domain. This suggests that criminal justice reform efforts to reduce police contact can improve population level wellbeing, and that clinical jail diversion and other decarceration efforts can further improve population-level wellbeing by preventing loss of individual and family member wellbeing

A performance comparison of automatic detection schemes in wide-area aerial imagery

Accurate and efficient detection of vehicles in wide-area aerial imagery is a fundamental task in understanding the automobile traffic patterns in an urban environment so as to help regulate the traffic flow. Vehicles with varying shapes and sizes, background clutter, occlusion, low-resolution and noise in the acquired images make the automatic detection of vehicles a challenging task. We present the performance analysis of six object detection algorithms for moving vehicle detection in low-resolution aerial image sequences. We compare the automatic detection results with manual detection, and evaluate the performance of the six object detection algorithms via several metrics.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Segmentation and Classification of 3-D Spots in FISH Images

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