Browsing by Subject "Bayes Theorem"
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Item A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures(MDPI, 2021-01-20) Smart, Utpal; Cihlar, Jennifer Churchill; Mandape, Sammed N.; Muenzler, Melissa; King, Jonathan L.; Budowle, Bruce; Woerner, August E.Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture's genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.Item Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006-2016(BioMed Central Ltd., 2019-08-23) Phanitchat, Thipruethai; Zhao, Bingxin; Haque, Ubydul; Pientong, Chamsai; Ekalaksananan, Tipaya; Aromseree, Sirinart; Thaewnongiew, Kesorn; Fustec, Benedicte; Bangs, Michael J.; Alexander, Neal; Overgaard, Hans J.BACKGROUND: Dengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases. METHODS: Data on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence. RESULTS: Dengue was predominant in the 5-14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province. CONCLUSIONS: There was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.Item Using big data for improving two surveillance systems: influenza surveillance using Google flu-related search query data and probationers absconding surveillance using chronological case notes data(2020-05) Liu, Jialiang; Suzuki, Sumihiro; Mun, Eun-Young; Nandy, Rajesh R.Abstract Title: Using big data for improving two surveillance systems: influenza surveillance using Google flu-related search query data and probationers absconding surveillance using chronological case notes data. Objectives: The overall goal of this dissertation is to explore the feasibility of using big data to create innovative strategies to improve two surveillance systems, influenza surveillance, and probationers absconding surveillance. Flu surveillance - Under the current gold standard for flu surveillance in the US conducted by the CDC, there is always a delay of up to three weeks between the occurrence of flu season onset and dissemination of this information. To this end, the first goal of this dissertation was to test an innovative strategy that applies a statistical detection algorithm to the near real-time seasonal flu activity data to predict the onset of flu season weeks prior to the flu season beginning. Probationers absconding surveillance - In the US legal system, probation is the most widely used alternative sanction to incarceration. However, there is a significant segment of probationers who fail to complete probation by absconding from supervision. Due to the limited financial resources and the increasing population of probationers, little effort has been made toward locating and examining these probation absconders. Our second goal was to explore words and phrases associated with probation absconders by applying natural language processing (NLP) techniques to official chronologic case notes written by probation officers. Methods: Flu surveillance - we applied the modified Bayesian online change point detection (BOCPD) algorithm to real-time flu activity data obtained from the AutoRegression with General Online (ARGO) data model. The ARGO model uses Google flu-related search query data and historical CDC flu activity data to estimate flu activity in a real-time fashion. We used change point detection methods on the ARGO data to predict the dates of flu season onset and compared them to those reported by the CDC from 2007 to 2015. In applying the BOCPD algorithm to the ARGO data, we developed systematic ways to satisfy the necessary assumptions of the BOCPD algorithm making it more robust and practical for flu surveillance, and we proposed a method to determine informative change points that may signal the onset of flu seasons. Probationer absconding surveillance - We applied a text regression method known as concise comparative summarization (CCS) method to text data generated from case notes of a random sample of adult misdemeanors and felony offenders who have received probation in Tarrant County, TX. Results and conclusions: Flu surveillance - Our strategy of flu surveillance exhibits a high accuracy of prediction with the proportion of correct prediction being 86%. Additionally, our strategy on average detected flu season onset three weeks prior to the official flu season onset. Probationer absconding surveillance - We found phrases such as "cannabinoids", "technical violations", "failed pay", and "transfer intake" to be associated with probation absconding. This suggests that probationers who had a history of using cannabinoids, violating probation conditions, failing to pay supervision fees during their probation periods, and those who were transfer cases tended more likely to be absconders. Meanwhile, phrases such as "everything going well", "travel", and "fees paid full" were found to be associated with probation completers. This implies that successful completers tended to have positive attitudes and willingness to share their personal life and feelings as well as having a stable income source to pay supervision fees. Currently, the case notes are kept only for record-keeping purposes. Our study identified previously unknown commonalities in the case notes of absconders and completers and may contribute to a new surveillance system that uses case notes systematically.