Relevant bibliographies by topics / Predictive risk modeling

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Academic literature on the topic 'Predictive risk modeling'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Predictive risk modeling"

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Katreddy, Venkata Senareddy. "Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40802.

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Healthcare providers and insurers face significant challenges in managing claims, particularly in detecting fraudulent activities and predicting high-cost claims. This paper proposes a methodology for predicting risks in healthcare claims using data analysis and machine learning techniques. By processing large-scale claims data, analyzing patterns, and building predictive models, this approach aims to improve risk management, operational efficiency, and cost savings. Keywords: Healthcare Claims, Risk Prediction, Data Analysis, Predictive Modeling
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Waheed, Shaikh Abdul, and P. Sheik Abdul Khader. "Healthcare Solutions for Children Who Stutter Through the Structural Equation Modeling and Predictive Modeling by Utilizing Historical Data of Stuttering." SAGE Open 11, no. 4 (2021): 215824402110581. http://dx.doi.org/10.1177/21582440211058195.

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Earlier studies established the role of demographic and temperamental features (DTFs) in the adaptation of childhood stuttering. However, these studies have been short on examining the latent interrelationships among DTFs and not utilizing them in predicting this disorder. This research article endeavors to examine latent interrelationships among DTFs in relation to childhood-stuttering. The purpose of the present is also to analyze whether DTFs can be utilized in predicting the likely risk of this speech disorder. Historical data on childhood stuttering was utilized for performing the invloved experiments of this research. “Structural-Equation-Modeling” (SEM) was applied to examine latent interrelationships among DTFs in relation to stuttering. The predictive analytics approach was employed to ensure whether DTFs of children can be utilized for predicting the likely risk of childhood-stuttering. SEM-based path analysis explored potential latent interrelationships among DTFs by separating them into categories of background and intermediate. By utilizing the same set of the DTFs, predictive models were able to classify children into stuttering and non-stuttering groups with optimal prediction accuracy. The outcomes of this study showed how the stuttering related historical data can be utilized in offering healthcare solutions for individuals with stuttering disorder. The outcomes of the present study also suggest that historical data on stuttering is a very rich source of hidden trends and patterns concerning this disorder. These hidden trends and patterns can be captured by applying a different type of structural and predictive modeling to understand the cause-and-effect relationship among variables in relation to stuttering. The SEM utilizes the cause-and-effect relationship among variables to explore latent-interrelationships between them. While predictive modeling utilizes the cause-and-effect relationship among variables to predict the possible risk of stuttering with optimal prediction accuracy.
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Vishwakarma, Saketh Kumar. "AI-Driven Predictive Risk Modelling for Aerospace Supply Chains." International Interdisciplinary Business Economics Advancement Journal 6, no. 5 (2025): 102–34. https://doi.org/10.55640/business/volume06issue05-06.

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In the aerospace supply chain, a complex, high-stakes ecosystem is at risk of multiple risk categories such as component shortage, cyber threats, and noncompliance with regulations. Traditional risk mitigation strategies are not enough. They are now offered as measures reactive to risks and static contingency plans. This paper investigates how AI-driven predictive risk modeling can break these limitations of the current risk management practices and allow risk management to change from reactionary to proactive across the aerospace supply chain. These models leverage the power of machine learning by poring over structured and unstructured data (telemetry data, supplier log files) and searching for patterns that predict future disruptions. Core technologies that can ingest and process data in real-time, like Apache Kafka and Apache Spark, support dynamic risk calculation. Combining with the domain expertise, they provide precision to the model and compliance framework (FAA, ITAR, AS9100) for legal compliance. The document also mentions some architectural shifts from monolith to microservice systems and the use of design patterns such as CQRS, the Strangler pattern, and ModelOps in the model deployment. Quantifiable benefits, as shown in a case study in a major aerospace OEM, include reduced downtime, decreased procurement times, and better prediction. Results suggest that stakeholders must be involved, ethical AI governance should be implemented, and iterative validation should be used to build trust and alignment in the system. Edge AI, blockchain, and quantum computing are moving in the right direction in the industry and predictive analytics. The guide is a strategic tool for converting their operation to systems with resilient and intelligent supply chains that the aerospace industry’s professionals aspire to embrace.
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Sadia Latif, Sami Ullah, Aafia Latif, Ghazanfar Ali, Muhammad Hassnain Azhar, and Salman Ali. "PREDICTIVE MODELING OF CARDIOVASCULAR DISEASE USING MACHINE LEARNING APPROACH." Kashf Journal of Multidisciplinary Research 2, no. 02 (2025): 207–32. https://doi.org/10.71146/kjmr288.

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The primary causes of death worldwide are Chronic Cardiac diseases. Accurately diagnose and predicting chronic cardiac disease is important to the proper treatment of cardiac patients before a heart attack occurs. The goal of accurate disease prediction will be achieved using a ML algorithm with health examination data. Early prediction of the risk factors of cardiac disease is critical for preventing heart disease. In our research, this is a follow-up study the statistical analysis will be used to assess the prediction of CCD as many high-risk factors (hypertension, smoking, high blood cholesterol, increasing age, male gender, being overweight) are involved. The heat-map cluster and machine learning algorithm provide interactive visualization for the classification of patients with different CCD stages. Early stages of cardiac patients are grouped into one cluster and advanced staged cardiac patients could be at high risk for the expeditious decline of heart function and should be closely monitored. The clustering heatmap provided a new predictive model for health care management for patients at high risk of rapid CCD progression. This model could help physicians make an accurate diagnosis of this progressive and complex disease.
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Muthuvel, Dr P. "Predictive Modeling for Cardiovascular Disease Risk Assessment." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3382–86. http://dx.doi.org/10.22214/ijraset.2024.58799.

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Abstract: Heart disease remains one of the leading causes of mortality worldwide. Early detection and risk assessment are crucial for effective prevention and management. This research paper presents a novel approach utilizing machine learning techniques, particularly decision trees, for predicting heart disease risk. The study utilizes a dataset sourced from the UCI Machine Learning Repository, encompassing diverse features such as age, gender, height, weight, cholesterol levels, and other relevant attributes. The proposed model aims to accurately classify individuals into risk categories based on their demographic and health-related information. Additionally, a user-friendly web application is developed using Python Flask, enabling users to input their data and receive instant risk assessments. Through rigorous experimentation and evaluation, the efficacy and reliability of the predictive model are demonstrated, offering valuable insights for early intervention and personalized healthcare strategies in the fight against heart disease.
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Toma, Milan, and Ong Chi Wei. "Predictive Modeling in Medicine." Encyclopedia 3, no. 2 (2023): 590–601. http://dx.doi.org/10.3390/encyclopedia3020042.

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Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.
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T. Benila, Christabel, and K. K. Thanammal. "Predictive modeling for cardiovascular disease risk assessment." i-manager's Journal on Data Science & Big Data Analytics 2, no. 1 (2024): 23. http://dx.doi.org/10.26634/jds.2.1.20811.

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Cardiovascular diseases (CVDs) continue to be the world's leading cause of death. It is imperative that accurate risk assessment and early intervention be implemented. This study proposes a predictive modeling framework, termed "HeartGuard," designed to assess an individual's risk of developing cardiovascular disease. Leveraging a diverse dataset comprising demographic information, lifestyle factors, medical history, and biomarker data, advanced machine learning techniques are employed to construct robust predictive models. The developed models incorporate features such as age, gender, blood pressure, cholesterol levels, smoking status, physical activity, and family history to estimate the probability of CVD occurrence within a specified timeframe. The evaluation of the models using cross-validation and independent validation datasets demonstrates their high accuracy, sensitivity, and specificity. HeartGuard offers a reliable tool for clinicians to identify individuals at heightened risk of cardiovascular disease, enabling targeted preventive measures and personalized healthcare interventions to mitigate the burden of CVD morbidity and mortality.
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Luxhoj, James T. "Predictive Analytics for Modeling UAS Safety Risk." SAE International Journal of Aerospace 6, no. 1 (2013): 128–38. http://dx.doi.org/10.4271/2013-01-2104.

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Babalola, Abayomi Danlami, Kayode Francis Akingbade, and Daniel Olakunle. "Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data." ABUAD Journal of Engineering Research and Development (AJERD) 7, no. 1 (2024): 153–59. http://dx.doi.org/10.53982/ajerd.2024.0701.15-j.

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Cardiovascular disease (CVD) remains the leading global cause of death, highlighting the urgent need for accurate risk assessment and prediction tools. Machine learning (ML) has emerged as a promising approach for CVD risk prediction, offering the potential to capture complex relationships between clinical and biometric data and patient outcomes. This study explores the application of support vector machines (SVMs), ensemble learning, and artificial neural networks (NNs) for predictive modeling of CVD in patients. The study utilizes a comprehensive dataset comprising demographic and biometric data of patients, including age, gender, blood pressure, cholesterol levels, and body mass index, features. SVMs, ensemble learning, and NNs are employed to construct predictive models based on these data. The performance of each model is evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The results demonstrate that all three models achieve accuracy performance in predicting CVD events, with AUC values ranging from 0.85 to 0.92. Ensemble learning exhibits the highest overall accuracy, while SVM and ANN demonstrate strengths in specific aspects of prediction. The study concludes that Machine learning algorithms, particularly ensemble learning, hold significant promise for improving CVD risk assessment. The integration of ML-based predictive models into demographic practice can facilitate early intervention, personalized treatment strategies, and improved patient outcomes.
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Wolff, Patricio, Manuel Graña, Sebastián A. Ríos, and Maria Begoña Yarza. "Machine Learning Readmission Risk Modeling: A Pediatric Case Study." BioMed Research International 2019 (April 15, 2019): 1–9. http://dx.doi.org/10.1155/2019/8532892.

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Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.
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Dissertations / Theses on the topic "Predictive risk modeling"

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Vanichbuncha, Tita. "Risk Factors and Predictive Modeling for Aortic Aneurysm." Thesis, Linköpings universitet, Statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80391.

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In 1963 – 1965, a large-scale health screening survey was undertaken in Sweden and this data set was linked to data from the national cause of death register. The data set involved more than 60,000 participants whose age at death less than 80 years. During the follow-up period until 2007, a total of 437 (338 males and 99 females) participants died from aortic aneurysm. The survival analysis, continuation ratio model, and logistic regression were applied in order to identify significant risk factors. The Cox regression after stratification for AGE revealed that SEX, Blood Diastolic Pressure (BDP), and Beta-lipoprotein (BLP) were the most significant risk factors, followed by Cholesterol (KOL), Sialic Acid (SIA), height, Glutamic Oxalactic Transaminase, Urinary glucose (URIN_SOC), and Blood Systolic Pressure (BSP). Moreover, SEX and BDP were found as risk factors in almost every age group. Furthermore, BDP was strongly significant in both male and female subgroup.   The data set was divided into two sets: 70 percent for the training set and 30 percent for the test set in order to find the best technique for predicting aortic aneurysm. Five techniques were implemented: the Cox regression, the continuation ratio model, the logistic regression, the back-propagated artificial neural network, and the decision tree. The performance of each technique was evaluated by using area under the receiver operating characteristic curve. In our study, the continuation ratio and the logistic regression outperformed among the other techniques.
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Ding, Xiuhua. "MODELING DEMENTIA RISK, COGNITIVE CHANGE, PREDICTIVE RULES IN LONGITUDINAL STUDIES." UKnowledge, 2016. http://uknowledge.uky.edu/epb_etds/9.

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Dementia is increasing recognized as a major problem to public health worldwide. Prevention and treatment strategies are in critical need. Nowadays, research for dementia usually featured as complex longitudinal studies, which provide extensive information and also propose challenge to statistical methodology. The purpose of this dissertation research was to apply statistical methodology in the field of dementia to strengthen the understanding of dementia from three perspectives: 1) Application of statistical methodology to investigate the association between potential risk factors and incident dementia. 2) Application of statistical methodology to analyze changes over time, or trajectory, in cognitive tests and symptoms. 3) Application of statistical learning methods to predict development of dementia in the future. Prevention of Alzheimer’s disease with Vitamin E and Selenium (PREADViSE) (7547 subjects included) and Alzheimer’s disease Neuroimaging Initiative (ADNI) (591 participants included) were used in this dissertation. The first study, “Self-reported sleep apnea and dementia risk: Findings from the PREADViSE Alzheimer’s disease prevention trial ”, shows that self-reported baseline history of sleep apnea was borderline significantly associated with risk of dementia after adjustment for confounding. Stratified analysis by APOE ε4 carrier status showed that baseline history of sleep apnea was associated with significantly increased risk of dementia in APOE ε4 non-carriers. The second study, “comparison of trajectories of episodic memory for over 10 years between baseline normal and MCI ADNI subjects,” shows that estimated 30% normal subjects at baseline assigned to group 3 and 6 stay stable for over 9 years, and normal subjects at baseline assigned to Group 1 (18.18%) and Group 5 (16.67%) were more likely to develop into dementia. In contrast to groups identified for normal subjects, all trajectory groups for MCI subjects at baseline showed the tendency to decline. The third study, “comparison between neural network and logistic regression in PREADViSE trial,” demonstrates that neural network has slightly better predictive performance than logistic regression, and also it can reveal complex relationships among covariates. In third study, the effect of years of education on response variable depends on years of age, status of APOE ɛ4 allele and memory change.
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Villa, Zapata Lorenzo Andrés. "Predictive Modeling Using a Nationally Representative Database to Identify Patients at Risk of Developing Microalbuminuria." Diss., The University of Arizona, 2014. http://hdl.handle.net/10150/333040.

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Background: Predictive models allow clinicians to more accurately identify higher- and lower-risk patients and make more targeted treatment decisions, which can help improve efficiency in health systems. Microalbuminuria (MA) is a condition characterized by the presence of albumin in the urine below the threshold detectable by a standard dipstick. Its presence is understood to be an early marker for cardiovascular disease. Therefore, identifying patients at risk for MA and intervening to treat or prevent conditions associated with MA, such as high blood pressure or high blood glucose, may support cost-effective treatment. Methods: The National Health and Nutrition Examination Survey (NHANES) was utilized to create predictive models for MA. This database includes clinical, medical and laboratory data. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized to validate the model. Univariate logistic regression was performed to identify variables related with MA. Stepwise multivariate logistic regression was performed to create the models. Model performance was evaluated using three criteria: 1) receiver operator characteristic (ROC) curves; 2) pseudo R-squared; and 3) goodness of fit (Hosmer-Lemeshow). The predictive models were then used to develop risk-scores. Results: Two models were developed using variables that had significant correlations in the univariate analysis (p-value<0.05). For Model A, variables included in the final model were: systolic blood pressure (SBP); fasting glucose; C-reactive protein; blood urea nitrogen (BUN); and alcohol consumption. For Model B, the variables were: SBP; glycohemoglobin; BUN; smoking status; and alcohol consumption. Both models performed well in the creation dataset and no significant difference between the models was found when they were evaluated in the validation set. A 0-18 risk score was developed utilizing Model A, and the predictive probability of developing MA was calculated. Conclusion: The predictive models developed provide new evidence about which variables are related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. Furthermore, the risk score developed using Model A may allow clinicians to more easily measure patient risk. Both predictive models will require external validation before they can be applied to other populations.
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Villaume, Erik. "Predicting customer level risk patterns in non-life insurance." Thesis, KTH, Matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103590.

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Several models for predicting future customer profitability early into customer life-cycles in the property and casualty business are constructed and studied. The objective is to model risk at a customer level with input data available early into a private consumer’s lifespan. Two retained models, one using Generalized Linear Model another using a multilayer perceptron, a special form of Artificial Neural Network are evaluated using actual data. Numerical results show that differentiation on estimated future risk is most effective for customers with highest claim frequencies.
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Fonti, Mary L. "A Predictive Modeling System: Early identification of students at-risk enrolled in online learning programs." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/367.

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Predictive statistical modeling shows promise in accurately predicting academic performance for students enrolled in online programs. This approach has proven effective in accurately identifying students who are at-risk enabling instructors to provide instructional intervention. While the potential benefits of statistical modeling is significant, implementations have proven to be complex, costly, and difficult to maintain. To address these issues, the purpose of this study is to develop a fully integrated, automated predictive modeling system (PMS) that is flexible, easy to use, and portable to identify students who are potentially at-risk for not succeeding in a course they are currently enrolled in. Dynamic and static variables from a student system (edX) will be analyzed to predict academic performance of an individual student or entire class. The PMS model framework will include development of an open-source Web application, application programming interface (API), and SQL reporting services (SSRS). The model is based on knowledge discovery database (KDD) approach utilizing inductive logic programming language (ILP) to analyze student data. This alternative approach for predicting academic performance has several unique advantages over current predictive modeling techniques in use and is a promising new direction in educational research.
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Rosile, Paul A. "Modeling Biotic and Abiotic Drivers of Public Health Risk from West Nile Virus in Ohio, 2002-2006." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405380213.

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Ebrahimvandi, Alireza. "Three Essays on Analysis of U.S. Infant Mortality Using Systems and Data Science Approaches." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96266.

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High infant mortality (IM) rates in the U.S. have been a major public health concern for decades. Many studies have focused on understanding causes, risk factors, and interventions that can reduce IM. However, death of an infant is the result of the interplay between many risk factors, which in some cases can be traced to the infancy of their parents. Consequently, these complex interactions challenge the effectiveness of many interventions. The long-term goal of this study is to advance the common understanding of effective interventions for improving health outcomes and, in particular, infant mortality. To achieve this goal, I implemented systems and data science methods in three essays to contribute to the understanding of IM causes and risk factors. In the first study, the goal was to identify patterns in the leading causes of infant mortality across states that successfully reduced their IM rates. I explore the trends at the state-level between 2000 and 2015 to identify patterns in the leading causes of IM. This study shows that the main drivers of IM rate reduction is the preterm-related mortality rate. The second study builds on these findings and investigates the risk factors of preterm birth (PTB) in the largest obstetric population that has ever been studied in this field. By applying the latest statistical and machine learning techniques, I study the PTB risk factors that are both generalizable and identifiable during the early stages of pregnancy. A major finding of this study is that socioeconomic factors such as parent education are more important than generally known factors such as race in the prediction of PTB. This finding is significant evidence for theories like Lifecourse, which postulate that the main determinants of a health trajectory are the social scaffolding that addresses the upstream roots of health. These results point to the need for more comprehensive approaches that change the focus from medical interventions during pregnancy to the time where mothers become vulnerable to the risk factors of PTB. Therefore, in the third study, I take an aggregate approach to study the dynamics of population health that results in undesirable outcomes in major indicators like infant mortality. Based on these new explanations, I offer a systematic approach that can help in addressing adverse birth outcomes—including high infant mortality and preterm birth rates—which is the central contribution of this dissertation. In conclusion, this dissertation contributes to a better understanding of the complexities in infant mortality and health-related policies. This work contributes to the body of literature both in terms of the application of statistical and machine learning techniques, as well as in advancing health-related theories.<br>Doctor of Philosophy<br>The U.S. infant mortality rate (IMR) is 71% higher than the average rate for comparable countries in the Organization for Economic Co-operation and Development (OECD). High infant mortality and preterm birth rates (PBR) are major public health concerns in the U.S. A wide range of studies have focused on understanding the causes and risk factors of infant mortality and interventions that can reduce it. However, infant mortality is a complex phenomenon that challenges the effectiveness of the interventions, and the IMR and PBR in the U.S. are still higher than any other advanced OECD nation. I believe that systems and data science methods can help in enhancing our understanding of infant mortality causes, risk factors, and effective interventions. There are more than 130 diagnoses—causes—for infant mortality. Therefore, for 50 states tracking the causes of infant mortality trends over a long time period is very challenging. In the first essay, I focus on the medical aspects of infant mortality to find the causes that helped the reduction of the infant mortality rates in certain states from 2000 to 2015. In addition, I investigate the relationship between different risk factors with infant mortality in a regression model to investigate and find significant correlations. This study provides critical recommendations to policymakers in states with high infant mortality rates and guides them on leveraging appropriate interventions. Preterm birth (PTB) is the most significant contributor to the IMR. The first study showed that a reduction in infant mortality happened in states that reduced their preterm birth. There exists a considerable body of literature on identifying the PTB risk factors in order to find possible explanations for consistently high rates of PTB and IMR in the U.S. However, they have fallen short in two key areas: generalizability and being able to detect PTB in early pregnancy. In the second essay, I investigate a wide range of risk factors in the largest obstetric population that has ever been studied in PTB research. The predictors in this study consist of a wide range of variables from environmental (e.g., air pollution) to medical (e.g., history of hypertension) factors. Our objective is to increase the understanding of factors that are both generalizable and identifiable during the early stage of pregnancy. I implemented state-of-the-art statistical and machine learning techniques and improved the performance measures compared to the previous studies. The results of this study reveal the importance of socioeconomic factors such as, parent education, which can be as important as biomedical indicators like the mother's body mass index in predicting preterm delivery. The second study showed an important relationship between socioeconomic factors such as, education and major health outcomes such as preterm birth. Short-term interventions that focus on improving the socioeconomic status of a mother during pregnancy have limited to no effect on birth outcomes. Therefore, we need to implement more comprehensive approaches and change the focus from medical interventions during pregnancy to the time where mothers become vulnerable to the risk factors of PTB. Hence, we use a systematic approach in the third study to explore the dynamics of health over time. This is a novel study, which enhances our understanding of the complex interactions between health and socioeconomic factors over time. I explore why some communities experience the downward spiral of health deterioration, how resources are generated and allocated, how the generation and allocation mechanisms are interconnected, and why we can see significantly different health outcomes across otherwise similar states. I use Ohio as the case study, because it suffers from poor health outcomes despite having one of the best healthcare systems in the nation. The results identify the trap of health expenditure and how an external financial shock can exacerbate health and socioeconomic factors in such a community. I demonstrate how overspending or underspending in healthcare can affect health outcomes in a society in the long-term. Overall, this dissertation contributes to a better understanding of the complexities associated with major health issues of the U.S. I provide health professionals with theoretical and empirical foundations of risk assessment for reducing infant mortality and preterm birth. In addition, this study provides a systematic perspective on the issue of health deterioration that many communities in the US are experiencing, and hope that this perspective improves policymakers' decision-making.
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Zhai, Jian. "Modeling Firms’ Productivity and Borrowers’ Default Risk." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25393.

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In the thesis, we study improvements in the precision of inefficiency estimation by considering the dependence information among the error terms in models of production. We propose that the production systems that account for technical and allocative inefficiencies offer a natural way to model dependence using vine copulas. We construct such vine copulas using a recently proposed family of bivariate copulas (APS-2 copula) that permit dependence between the magnitude (but not the sign) of the allocative inefficiency and the magnitude of the technical inefficiency, and a Gaussian copula. We show how to estimate such models and argue that they better reflect dependencies that arise in practice. Following that we study around 200 features of personal loan customers, and apply machine learning models to see how these characteristics can help with risk measurement. The cost for different misclassification errors are different for personal loan providers. Furthermore, the misclassification costs for each example are different. Therefore, we propose an example-dependent cost-sensitive gradient boosting model and apply the method to a personal loan dataset.
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Mesgarpour, Mohsen. "Predictive risk modelling of hospital emergency readmission, and temporal comorbidity index modelling using machine learning methods." Thesis, University of Westminster, 2017. https://westminsterresearch.westminster.ac.uk/item/q3031/predictive-risk-modelling-of-hospital-emergency-readmission-and-temporal-comorbidity-index-modelling-using-machine-learning-methods.

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This thesis considers applications of machine learning techniques in hospital emergency readmission and comorbidity risk problems, using healthcare administrative data. The aim is to introduce generic and robust solution approaches that can be applied to different healthcare settings. Existing solution methods and techniques of predictive risk modelling of hospital emergency readmission and comorbidity risk modelling are reviewed. Several modelling approaches, including Logistic Regression, Bayes Point Machine, Random Forest and Deep Neural Network are considered. Firstly, a framework is proposed for pre-processing hospital administrative data, including data preparation, feature generation and feature selection. Then, the Ensemble Risk Modelling of Hospital Readmission (ERMER) is presented, which is a generative ensemble risk model of hospital readmission model. After that, the Temporal-Comorbidity Adjusted Risk of Emergency Readmission (T-CARER) is presented for identifying very sick comorbid patients. A Random Forest and a Deep Neural Network are used to model risks of temporal comorbidity, operations and complications of patients using the T-CARER. The computational results and benchmarking are presented using real data from Hospital Episode Statistics (HES) with several samples across a ten-year period. The models select features from a large pool of generated features, add temporal dimensions into the models and provide highly accurate and precise models of problems with complex structures. The performances of all the models have been evaluated across different timeframes, sub-populations and samples, as well as previous models.
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Atan, Ismail Bin. "Stochastic modelling of streamflow for predicting seasonal flood risk." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242379.

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Hydrological time series are often asymmetric in time, insomuch as rises are more rapid than recessions, as well as having highly skewed marginal distributions. A two-stage transformation is proposed for deseasonalised daily average flow series. Rises are stretched, and recessions are squashed until the series is symmetric over time. An autoregressive moving average (ARMA) model is then fitted to the natural logarithms of this new series The residuals from the ARMA model are represented by Weibull distributions. Seasonal flood risks, as daily average flows, are estimated by simulation. However, floods are often measured as peak flows rather than daily average flows, although both measures are relevant, and the use of growth factors to allow for this is demonstrated. The method is demonstrated with 24 years of daily flows from River Cherwell in the south of England, a 40-years record from the upper reaches of the Thames and 21-years record from the River Coquet in the north-east of England. Seasonal estimates of flood risk are given, and these can be conditioned on catchment wetness at the time of prediction. Comparisons with other methods which allow for time irreversibility are also made. One is ARMA models with exogenous input, in this case rainfall, which will, because of its intermittent nature, impact a natural time irreversibility to the streamflow series. A disadvantage of these models is that they require rainfall data in addition to the streamflow record. A second is the development of a class of shot noise models, which naturally generate highly time irreversibility series. This is the Neyman-Scott model. But, despite its attractive physical interpretation it is inevitably less flexible than the two stage transformation because it has fewer parameters. Although it was found to provide a good fit to daily data it is less convincing for the extremes. Overall the two stage transformation (TST) compared favourably with both models.
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Books on the topic "Predictive risk modeling"

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Howard, Brill. Predictive modeling: Improving margins by identifying and targeting high-risk populations. Healthcare Intelligence Network, 2005.

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Jones, Stewart, and David A. Hensher, eds. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008. http://dx.doi.org/10.1017/cbo9780511754197.

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Costa, Rui. Predictive Modeling and Risk Assessment. Springer, 2009.

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Duncan, Ian. Healthcare Risk Adjustment & Predictive Modeling. ACTEX Learning, 2018.

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Costa, Rui. Predictive Modeling and Risk Assessment. Springer, 2010.

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Healthcare risk adjustment and predictive modeling. ACTEX Publications, 2011.

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Predictive Safety Analytics: Reducing Risk Through Modeling and Machine Learning. Taylor & Francis Group, 2023.

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Predictive Safety Analytics: Reducing Risk Through Modeling and Machine Learning. Taylor & Francis Group, 2023.

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Predictive Safety Analytics: Reducing Risk Through Modeling and Machine Learning. Taylor & Francis Group, 2023.

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Predictive Safety Analytics: Reducing Risk Through Modeling and Machine Learning. CRC Press LLC, 2023.

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Book chapters on the topic "Predictive risk modeling"

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Steif, Ken. "Geospatial Risk Modeling - Predictive Policing." In Public Policy Analytics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003054658-5.

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Ryan, Shannon. "Numerical Simulation in Micrometeoroid and Orbital Debris Risk Assessment." In Predictive Modeling of Dynamic Processes. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0727-1_22.

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Shelton, Brett E., Juan Yang, Jui-Long Hung, and Xu Du. "Two-Stage Predictive Modeling for Identifying At-Risk Students." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99737-7_61.

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Wang, Tingyan, Robin G. Qiu, and Ming Yu. "Multiple-Disease Risk Predictive Modeling Based on Directed Disease Networks." In Smart Service Systems, Operations Management, and Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_21.

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Lakshana, S., P. Ashok, and Abhijit Chirputkar. "Predictive Modeling of Cardiovascular Disease Risk with IoT and ML." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6103-6_27.

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Lin, Jason, Benke Qu, Xing Wang, Stephen M. George, and Jyh-Charn Liu. "Risk Management in Asymmetric Conflict: Using Predictive Route Reconnaissance to Assess and Mitigate Threats." In Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_42.

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Furlanello, Cesare, and Stefano Merle. "Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_21.

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Bhanawat, Hemant, and Alex Khang. "Theoretical Analysis and Data Modeling of the Influence of Shadow Banking on Systemic Risk." In Data-Driven Modelling and Predictive Analytics in Business and Finance. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781032618845-10.

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Vaithianathan, Rhema, Stephanie Cuccaro-Alamin, and Emily Putnam-Hornstein. "Improving Child Welfare Practice Through Predictive Risk Modeling: Lessons from the Field." In Strengthening Child Safety and Well-Being Through Integrated Data Solutions. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36608-6_8.

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Sheikh, Md Mamun, Shahera Hossain, and Md Atiqur Rahman Ahad. "Predictive Modeling for Heatstroke Risk Forecasting Integrating Physiological Features Using Ensemble Classifier." In Activity, Behavior, and Healthcare Computing. CRC Press, 2025. https://doi.org/10.1201/9781032648422-17.

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Conference papers on the topic "Predictive risk modeling"

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Maheshwari, Himani, Dharminder Yadav, Umesh Chandra, Abhishek Kaleroun, and Ashulekha Gupta. "Predictive Cardiovascular Risk Modeling Using Machine Learning Techniques." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840904.

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Chithambaramani, R., M. Sankar, P. Sivaprakash, Manivannan D, J. Vimala Ithayan, and Prakash Mohan. "Predictive Modeling for Lung Cancer Risk Assessment Using Deep Learning." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696048.

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Chen, Danni, Chao Lu, and Jianwei Gong. "Driver-Centric Predictive Risk Map Modeling via Deep Reinforcement Learning." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864604.

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Singh, Preet, K. R. Ramkumar, Sonam Mittal, and Taniya Hasija. "Predictive Modeling of Brain Stroke Risk Using Supervised Machine Learning Techniques." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841156.

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S, Abdulkadar, Dharun S, Kirthivashan V, and Janani B M.E. "Enhanced Predictive Modeling for Myocardial Infarction Risk Assessment through Multimodal Data Integration." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10941070.

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Wang, Yuhan, Zhen Xu, Yue Yao, Jinsong Liu, and Jiating Lin. "Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929474.

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Ruthra, R., Naveen S, Sreesh Kalingaraya P, and Marshall J. "Predictive Modeling for Safety Risk Assessment in Bridge Construction using ARIMA and LSTM." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988260.

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Dighe, Akshada, and Bindu Garg. "Predictive Modeling of Child Identity Risk using Machine and Deep Learning: A Systematic Literature Review." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912169.

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Karapintzou, Efterpi, Vassilios Tsakanikas, Dimitrios Kikidis, et al. "AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820488.

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Tajallipour, Nima, Patrick J. Teevens, Wale Akanni, and Sridhar Arumugam. "Application of Internal Corrosion Predictive Modeling (ICPM) in Pipeline Integrity Risk Management of Oil and Gas Production Fields." In CORROSION 2014. NACE International, 2014. https://doi.org/10.5006/c2014-4067.

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Abstract This paper summarizes a comprehensive internal corrosion integrity risk assessment which was conducted for six different oil and gas production fields. First, the internal corrosion modeling technique was applied to assess the risk of internal corrosion for all the oil emulsion (OE), sour gas (SG), salt water injection (SW) and 15% of sweet natural gas (NG) pipelines which were in-service at the time of this investigation. Modeling results were then used to conduct a pipeline integrity risk assessment for the investigated production fields. This pipeline risk assessment follows in-part the non-mandatory guidelines outlined in CSA Z662-11(*)1 and the scope of this paper, only covers the risk of internal corrosion degradation. The results of the risk assessment is a ranking of each pipeline based on known and calculated parameters which affect the “likelihood” or probability of a corrosion related event occurring and the “consequence” of a corrosion related failure.
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Reports on the topic "Predictive risk modeling"

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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Harris, Aubrey, Nathan Richards, and S. McKay. Defining levels of effort for ecological models. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47642.

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While models are useful tools for decision-making in environmental management, the question arises about the level of effort required to develop an effective model for a given application. In some cases, it is unclear whether more analysis would lead to choosing a better course of action. This technical note (TN) examines the role of ecological model complexity in ecosystem management. First, model complexity is examined through the lens of risk informed planning. Second, a framework is presented for categorizing five different levels of effort that range from conceptual models to detailed predictive tools. This framework is proposed to enhance communication and provide consistency in ecological modeling applications. Third, the level of effort framework is applied to a set of models in the Middle Rio Grande River system to demonstrate the framework’s utility and application. Ultimately, this TN seeks to guide planners in determining an appropriate level of effort relative to risks associated with uncertainty and resource availability for a given application.
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Russo, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7592658.bard.

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The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geologic features comprising the vadose zone; (ii) to develop a complementary set of data analysis tools for discerning the fractal properties of hydraulic and transport parameters of highly heterogeneous vadose zone; (iii) to develop and test the associated computational methods for probabilistic analysis of flow and transport in highly heterogeneous subsurface environments; and (iv) to apply the computational framework to design an “optimal” observation network for monitoring and forecasting the fate and migration of contaminant plumes originating from agricultural activities. During the course of the project, we modified the third objective to include additional computational method, based on the notion that the heterogeneous formation can be considered as a mixture of populations of differing spatial structures. Regarding uncertainly analysis, going beyond approaches based on mean and variance of system states, we succeeded to develop probability density function (PDF) solutions enabling one to evaluate probabilities of rare events, required for probabilistic risk assessment. In addition, we developed reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events Regarding flow and transport in variably saturated, spatially heterogeneous formations associated with fine- and coarse-textured embedded soils (FTES- and CTES-formations, respectively).We succeeded to develop first-order and numerical frameworks for flow and transport in three-dimensional (3-D), variably saturated, bimodal, heterogeneous formations, with single and dual porosity, respectively. Regarding the sampling problem defined as, how many sampling points are needed, and where to locate them spatially in the horizontal x₂x₃ plane of the field. Based on our computational framework, we succeeded to develop and demonstrate a methdology that might improve considerably our ability to describe quntitaively the response of complicated 3-D flow systems. The results of the project are of theoretical and practical importance; they provided a rigorous framework to modeling water flow and solute transport in a realistic, highly heterogeneous, composite flow system with uncertain properties under-specified by data. Specifically, they: (i) enhanced fundamental understanding of the basic mechanisms of field-scale flow and transport in near-surface geological formations under realistic flow scenarios, (ii) provided a means to assess the ability of existing flow and transport models to handle realistic flow conditions, and (iii) provided a means to assess quantitatively the threats posed to groundwater by contamination from agricultural sources.
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Bruce. L51942 Refinement of Cooling Rate Prediction Methods for In-Service Welds. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0010435.

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Welds made onto in-service pipeline are particularly susceptible to hydrogen cracking because of the fast weld cooling rates that tend to result from the ability of the flowing contents to remove heat from the pipe wall. The most commonly used procedures for controlling the risk of hydrogen cracking rely on the use of a sufficiently high heat input level. Two methods currently exist for predicting required heat input levels for welds made onto in-service pipelines: thermal analysis computer modeling and the heat-sink capacity measurement method. The objective of this project was to refine these two complementary methods, and to investigate alternative approaches. The project was divided into three distinct tasks: further refinement of the PRCI Thermal Analysis Model for Hot Tap Welding, standardization of heat-sink capacity measurement, and investigation of alternative approaches to cooling rate prediction. The primary link between the PRCI model and the heat-sink capacity measurement method is the ability of the model to predict the heat-sink capacity of an operating pipeline. Detailed descriptions of user interface modifications required to incorporate the ability to enter the individual heating parameters of interest and have the model calculate the heating rate were developed.
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Panek, Krol, and Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Pipeline Research Council International, Inc. (PRCI), 2016. http://dx.doi.org/10.55274/r0010858.

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AERMOD is the EPA recommended dispersion modeling tool for evaluating impacts from typical compressor station engine sources. This is a companion document to two previous PRCI reports that addressed AERMOD Fortran compiler issues and a subsequent report that examined AERMOD Plume Volume Molar ratio Method (PVMRM) issues that lead to conservative model over-predictions. This report further explores AERMOD plume rise and volume estimates as a possible cause or contributor of model over-prediction and resulting plume chemistry concerns. AERMOD over-prediction bias has significant negative implications for permitting new sources, permit renewal for existing sources, and NAAQS compliance analyses, where modeled impacts are compared to the NO2 NAAQS at or beyond the facility fenceline. AERMOD conservatism also impacts state agency State Implementation Plans and resulting control strategies. Permitting requirements associated with the new 1-hour standard could impose unnecessary controls, overly stringent controls, and a significant compliance burden. Where mitigation may be warranted, costs will escalate due to �over-control� in response to model conservatism and deficiencies in model performance.
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Dinovitzer. L52243 Modeling of Delayed Hydrogen Cracking for In-Service Welds. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0010916.

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The objective of this research project was to demonstrate and develop a means of predicting the inspection delay time such that hydrogen cracking would not occur after inspection for in-service welds. Hydrogen cracking is an issue for in-service welding since this form of cracking may occur a significant time after the weldment has cooled to ambient temperatures. Hydrogen cracking has three necessary conditions including a tensile stress, the presence of hydrogen and susceptible microstructure. After weld cooling the microstructure is stable and it was assumed that the local weld stress state is constant, therefore, the potential for cracking is only affected by the change in local hydrogen concentration. It was asserted that after the peak local hydrogen concentration is reached the risk of cracking is over. The current project described the theory, validation and use of the BMT Hydrogen Diffusion and Cracking model. The results of this project was the demonstration of peak hydrogen delay time trends with in-service welding conditions and the development of formulae that estimate the time to peak hydrogen in a weldment as a surrogate for the time to hydrogen cracking. The results of this project provide information useful in establishing in-service weld inspection delay times for fillet weld applications.
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Perdigão, Rui A. P. Strengthening Multi-Hazard Resilience with Quantum Aerospace Systems Intelligence. Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/240301.

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The present work further enhances and deploys our Quantum Aerospace Systems Intelligence technologies (DOI: 10.46337/quasi.230901) onto Multi-Hazard risk assessment and action, from sensing and prediction to modelling, decision support and active response, towards strengthening its fundamental knowledge, awareness and resilience in the face of multi-domain challenges. Moreover, it introduces our updated post-quantum aerospace engineering ecosystem for empowering active system dynamic capabilities to mitigate or even counter multi-hazard threats from space, leveraging our high energy technological physics solutions acting across coevolutionary space-times. These developments are further articulated with our latest Synergistic Nonlinear Quantum Wave Intelligence Networks suite of technologies (DOI: 10.46337/240118), vastly extending the operational capabilities of novel quantum and post-quantum systems to critically adverse thermodynamic conditions e.g. those pertaining situational action across real-world environmental and security theaters of operation.
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Aguilar, Glenn, Dan Blanchon, Hamish Foote, Christina Pollonais, and Asia Mosee. Queensland Fruit Fly Invasion of New Zealand: Predicting Area Suitability Under Future Climate Change Scenarios. Unitec ePress, 2017. http://dx.doi.org/10.34074/pibs.rs22015.

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The Queensland fruit fly (Bactrocera tryoni) is a significant horticultural pest in Australia, and has also established in other parts of the Pacific. There is a significant risk to New Zealand of invasion by this species, and several recent incursions have occurred. The potential effects of climate change on the distribution and impacts of invasive species are well known. This paper uses species distribution modelling using Maxent to predict the suitability of New Zealand to the Queensland fruit fly based on known occurrences worldwide and Bioclim climatic layers. Under current climatic conditions the majority of the country was generally in the lower range, with some areas in the medium range. Suitability prediction maps under future climate change conditions in 2050 and 2070, at lower emission (RCP 2.6) and higher emission (RCP 8.5) scenarios generally show an increase in suitability in both the North and South Islands. Calculations of the shift of suitable areas show a general movement of the centroid towards the south-east, with the higher emission scenario showing a greater magnitude of movement.
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Torres, Marissa, Norberto Nadal-Caraballo, and Alexandros Taflanidis. Rapid tidal reconstruction for the Coastal Hazards System and StormSim part II : Puerto Rico and U.S. Virgin Islands. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41482.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the continuing efforts towards incorporating rapid tidal time-series reconstruction and prediction capabilities into the Coastal Hazards System (CHS) and the Stochastic Storm Simulation System (StormSim). The CHS (Nadal-Caraballo et al. 2020) is a national effort for the quantification of coastal storm hazards, including a database and web tool (https://chs.erdc.dren.mil) for the deployment of results from the Probabilistic Coastal Hazard Analysis (PCHA) framework. These PCHA products are developed from regional studies such as the North Atlantic Coast Comprehensive Study (NACCS) (Nadal-Caraballo et al. 2015; Cialone et al. 2015) and the ongoing South Atlantic Coast Study (SACS). The PCHA framework considers hazards due to both tropical and extratropical cyclones, depending on the storm climatology of the region of interest. The CHS supports feasibility studies, probabilistic design of coastal structures, and flood risk management for coastal communities and critical infrastructure. StormSim (https://stormsim.erdc.dren.mil) is a suite of tools used for statistical analysis and probabilistic modeling of historical and synthetic storms and for stochastic design and other engineering applications. One of these tools, the Coastal Hazards Rapid Prediction System (CHRPS) (Torres et al. 2020), can perform rapid prediction of coastal storm hazards, including real-time hurricane-induced flooding. This CHETN discusses the quantification and validation of the Advanced Circulation (ADCIRC) tidal constituent database (Szpilka et al. 2016) and the tidal reconstruction program Unified Tidal analysis (UTide) (Codiga 2011) in the Puerto Rico and U.S. Virgin Islands (PR/USVI) coastal regions. The new methodology discussed herein will be further developed into the Rapid Tidal Reconstruction (RTR) tool within the StormSim and CHS frameworks.
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