Refining Risk Estimates Using Models (Chapter 4)

In 2004 nineteen scientists from fourteen institutions in seven countries
collaborated in the landmark study described in chapter 2 (Thomas et al., 2004a). This chapter provides an overview of results of studies published subsequently and assesses how much, and why, new results differ from those of Thomas et al.
Some species distribution modeling (SDM) studies are directly comparable to the Thomas et al. estimates. Others using somewhat different methods nonetheless illuminate whether the original estimates were of the right order of magnitude. Climate similarity models (Williams et al., 2007; Williams and Jackson, 2007), biome, and vegetation dynamic models (Perry and Enright, 2006) have also been
applied in the context of climate change, providing interesting opportunities
for comparison and cross-validation with results from SDMs.
This chapter concludes with an assessment of whether the range of extinction risk estimates presented in 2004 can be narrowed, and whether the mean estimate should be revised upward or downward. To set the stage for these analyses, the chapter begins with brief reviews of advances in climate modeling and species modeling since 2004.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

Assessing 1D Loss Models for the Off-Design Performance Prediction of Automotive Turbocharger Compressors

Single-Zone modelling is used to assess three 1D impeller loss model collections. An automotive turbocharger centrifugal compressor is used for evaluation. The individual 1D losses are presented relative to each other at three tip speeds to provide a visual description of each author’s perception of the relative importance of each loss. The losses are compared with their resulting prediction of pressure ratio and efficiency, which is further compared with test data; upon comparison, a combination of the 1D loss collections is identified as providing the best performance prediction. 3D CFD simulations have also been carried out for the same geometry using a single passage model. A method of extracting 1D losses from CFD is described and utilised to draw further comparisons with the 1D losses. A 1D scroll volute model has been added to the single passage CFD results; good agreement with the test data is achieved. Short-comings in the existing 1D loss models are identified as a result of the comparisons with 3D CFD losses. Further comparisons are drawn between the predicted 1D data, 3D CFD simulation results, and the test data using a nondimensional method to highlight where the current errors exist in the 1D prediction.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

GEP analysis validates high risk MDS and acute myeloid leukemia post MDS mice models and highlights novel dysregulated pathways

BACKGROUND: In spite of the recent discovery of genetic mutations in most myelodysplasic (MDS) patients, the pathophysiology of these disorders still remains poorly understood, and only few in vivo models are available to help unravel the disease.

METHODS: We performed global specific gene expression profiling and functional pathway analysis in purified Sca1+ cells of two MDS transgenic mouse models that mimic human high-risk MDS (HR-MDS) and acute myeloid leukemia (AML) post MDS, with NRASD12 and BCL2 transgenes under the control of different promoters MRP8NRASD12/tethBCL-2 or MRP8[NRASD12/hBCL-2], respectively.

RESULTS: Analysis of dysregulated genes that were unique to the diseased HR-MDS and AML post MDS mice and not their founder mice pointed first to pathways that had previously been reported in MDS patients, including DNA replication/damage/repair, cell cycle, apoptosis, immune responses, and canonical Wnt pathways, further validating these models at the gene expression level. Interestingly, pathways not previously reported in MDS were discovered. These included dysregulated genes of noncanonical Wnt pathways and energy and lipid metabolisms. These dysregulated genes were not only confirmed in a different independent set of BM and spleen Sca1+ cells from the MDS mice but also in MDS CD34+ BM patient samples.

CONCLUSIONS: These two MDS models may thus provide useful preclinical models to target pathways previously identified in MDS patients and to unravel novel pathways highlighted by this study.

Development of open models and tools for seismic risk assessment: application to Portugal

The exponential growth of the world population has led to an increase of settlements often located in areas prone to natural disasters, including earthquakes. Consequently, despite the important advances in the field of natural catastrophes modelling and risk mitigation actions, the overall human losses have continued to increase and unprecedented economic losses have been registered. In the research work presented herein, various areas of earthquake engineering and seismology are thoroughly investigated, and a case study application for mainland Portugal is performed. Seismic risk assessment is a critical link in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible numerical tools and software. In the present work, an open-source platform for seismic hazard and risk assessment is developed. This software is capable of computing the distribution of losses or damage for an earthquake scenario (deterministic event-based) or earthquake losses due to all the possible seismic events that might occur within a region for a given interval of time (probabilistic event-based). This effort has been developed following an open and transparent philosophy and therefore, it is available to any individual or institution. The estimation of the seismic risk depends mainly on three components: seismic hazard, exposure and vulnerability. The latter component assumes special importance, as by intervening with appropriate retrofitting solutions, it may be possible to decrease directly the seismic risk. The employment of analytical methodologies is fundamental in the assessment of structural vulnerability, particularly in regions where post-earthquake building damage might not be available. Several common methodologies are investigated, and conclusions are yielded regarding the method that can provide an optimal balance between accuracy and computational effort. In addition, a simplified approach based on the displacement-based earthquake loss assessment (DBELA) is proposed, which allows for the rapid estimation of fragility curves, considering a wide spectrum of uncertainties. A novel vulnerability model for the reinforced concrete building stock in Portugal is proposed in this work, using statistical information collected from hundreds of real buildings. An analytical approach based on nonlinear time history analysis is adopted and the impact of a set of key parameters investigated, including the damage state criteria and the chosen intensity measure type. A comprehensive review of previous studies that contributed to the understanding of the seismic hazard and risk for Portugal is presented. An existing seismic source model was employed with recently proposed attenuation models to calculate probabilistic seismic hazard throughout the territory. The latter results are combined with information from the 2011 Building Census and the aforementioned vulnerability model to estimate economic loss maps for a return period of 475 years. These losses are disaggregated across the different building typologies and conclusions are yielded regarding the type of construction more vulnerable to seismic activity.

Environmental risk factors for the initial acquisition of Pseudomonas aeruginosa in young children with cystic fibrosis

Thesis (Ph.D.)--University of Washington, 2013

Prediction and Privacy in Healthcare Analytics

Thesis (Master's)--University of Washington, 2016-03

Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

Geostatistics has been successfully used to analyze and characterize the spatial variability of environmental properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work universal block kriging is novelty used to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents that are very valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies might be very helpful in the future to validate dispersion models.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

How bank business models relate with risk?: The case of Portugal in times of financial crisis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Parent Prediction of Autism Spectrum Disorder in Infants At Risk: A Follow-up Study

Later-born siblings of children with autism spectrum disorder (ASD) are considered at biological risk for ASD and the broader autism phenotype. Early screening may detect early signs of ASD and facilitate intervention as soon as possible. This follow-up study revisits and re-examines a second-degree autism screener for children at biological risk of autism, the Parent Observation Early Markers Scale (POEMS, Feldman et al., 2012). Using available follow-up information, 110 children (the original 108 infants plus 2 infants recruited after the completion of the original study) were divided into three groups: diagnosed group (n = 13), lost diagnosis group (n = 5), and undiagnosed group (n = 92). The POEMS continued to show acceptable predictive validity. The POEMS total scores and mean number of elevated items were significantly higher in the diagnosed group than the undiagnosed group. The lost diagnosis group did not differ from the undiagnosed group on POEMS total scores and elevated items at any age, but the lost diagnosis group had significantly lower total scores and number of elevated items than the diagnosed group starting at 18 months. Both ASD core and subsidiary behaviours differentiated the diagnosed and undiagnosed groups from 9−36 months of age. Using 70 as a cut-off score, sensitivity, specificity, and positive predictive value (PPV) were .69, .84, and .38, respectively. The study provides further evidence that the POEMS may serve as a low-cost early screener for ASD in at risk children and pinpoint specific developmental and behavioural problems that may be amenable to very early intervention.