Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.

ABSTRACT: We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting.

SUBMITTER: Liu J

PROVIDER: S-EPMC5994772 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.

Liu Jingyu J Piegorsch Walter W WW Schissler A Grant AG Cutter Susan L SL

Journal of the Royal Statistical Society. Series A, (Statistics in Society) 20171010 3

We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, where ...[more]

PMID: 29904240

Similar Datasets

Common statistical patterns in urban terrorism.

Project description:The underlying reasons behind modern terrorism are seemingly complex and intangible. Despite diverse causal mechanisms, research has shown that there exists general statistical patterns at the global scale that can shed light on human confrontation behaviour. While many policing and counter-terrorism operations are conducted at a city level, there has been a lack of research in building city-level resolution prediction engines based on statistical patterns. For the first time, the paper shows that there exist general commonalities between global cities under frequent terrorist attacks. By examining over 30 000 geo-tagged terrorism acts over 7000 cities worldwide from 2002 to today, the results show the following. All cities experience attacks A that are uncorrelated to the population and separated by a time interval t that is negative exponentially distributed with a death-toll per attack that follows a power-law distribution. The prediction parameters yield a high confidence of explaining up to 87% of the variations in frequency and 89% in the death-toll data. These findings show that the aggregate statistical behaviour of terror attacks are seemingly random and memoryless for all global cities. They enabled the author to develop a data-driven city-specific prediction system, and we quantify its information-theoretic uncertainty and information loss. Further analysis shows that there appears to be an increase in the uncertainty over the predictability of attacks, challenging our ability to develop effective counter-terrorism capabilities.

| S-EPMC6774967 | biostudies-literature

Public health assessment of potential biological terrorism agents.

Project description: Not available

| S-EPMC2732458 | biostudies-literature

Use of benchmark dose models in risk assessment for occupational handlers of eight pesticides used in pome fruit production.

Project description:The benchmark dose has been frequently recommended for the creation of points of departure for regulatory dose limits, but many regulations, including pesticide risk assessment and registration in the United States, continues to rely on NOAEL methods as the OECD toxicological standard methods recommend. This study used data from studies in support of pesticide registration for eight different compounds to build dose-response models and calculate benchmark doses and confidence limits. The results were compared to the NOAEL of the same study. A probabilistic estimate of dose was compared with all points of departure to demonstrate differences in the protective ability of each different selected limit. While neither the BMD/BMDL nor the NOAEL was consistently more protective, the advantage of using the BMD in quantifying the uncertainty of the point of departure is highlighted, and the feasibility of using current OECD-guideline studies for derivation of a BMD is demonstrated in these cases.

| S-EPMC6937384 | biostudies-literature

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley.

Project description:In our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks.

| S-EPMC9005627 | biostudies-literature

Methods for jurisdictional vulnerability assessment of opioid-related outcomes.

Project description:In 2020, an estimated 2.7 million people in the US had opioid use disorder, increasing their risk of opioid-related morbidity and mortality. While jurisdictional vulnerability assessments (JVA) of opioid-related outcomes have been conducted previously in the US, there has been no unifying methodological framework. Between 2019 and 2021, we prepared ten JVAs, in collaboration with the Council of State and Territorial Epidemiologists, the Centers for Disease Control and Prevention, and state public health agencies, to evaluate the risk for opioid-involved overdose (OOD) fatalities and related consequences. Our aim is to share the framework we developed for these ten JVAs, based on our study of the work of Van Handel et al. from 2016, as well as a summary of 18 publicly available assessments of OOD or associated hepatitis C virus infection vulnerability. We developed a three-tiered framework that can be applied by jurisdictions based on the number of units of analysis (e.g., counties, ZIP Codes, census tracts): under 10 (Tier 1), 10 to <50 (Tier 2), and 50 or more (Tier 3). We calculated OOD vulnerability indices based on variable ranks, weighted variable ranks, or multivariable regressions, respectively, for the three tiers. We developed thematic maps, conducted spatial analyses, and visualized service provider locations, drive-time service areas, and service accessibility relative to OOD risk. The methodological framework and examples of our findings from several jurisdictions can be used as a foundation for future assessments and help inform policies to mitigate the impact of the opioid overdose crisis.

| S-EPMC11371392 | biostudies-literature

Benchmark dose risk analysis with mixed-factor quantal data in environmental risk assessment.

Project description:Benchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response (BMR). Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or more additional, secondary, qualitative factor(s) may collude to affect the adverse outcome, such that the risk changes with differential levels of the secondary factor. This paper extends the single-dose BMD paradigm to a mixed-factor setting with a secondary qualitative factor possessing two levels. With focus on quantal-response data and using a generalized linear model with a complementary-log link function, we derive expressions for BMD and BMDL. We study the operating characteristics of six different multiplicity-adjusted approaches to calculate the BMDL, using Monte Carlo evaluations. We illustrate the calculations via an example data set from environmental carcinogenicity testing.

| S-EPMC8330878 | biostudies-literature

A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models.

Project description:Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.

| S-EPMC8945737 | biostudies-literature

Protein models docking benchmark 2.

Project description:Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C(?) RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the "real case scenario," as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.

| S-EPMC4400263 | biostudies-literature

Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment.

Project description:This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose-response data and when there are competing model classes for the dose-response function. Strategies involving a two-step approach, a model-averaging approach, a focused-inference approach, and a nonparametric approach based on a PAVA-based estimator of the dose-response function are described and compared. Attention is raised to the perils involved in data "double-dipping" and the need to adjust for the model-selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal-response data set from a carcinogenecity study is provided.

| S-EPMC5173468 | biostudies-literature

Benchmark dose profiles for joint-action quantal data in quantitative risk assessment.

Project description:Benchmark analysis is a widely used tool in public health risk analysis. Therein, estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a prespecified Benchmark Response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This article demonstrates how the benchmark modeling paradigm can be expanded from the single-dose setting to joint-action, two-agent studies. Focus is on response outcomes expressed as proportions. Extending the single-exposure setting, representations of risk are based on a joint-action dose-response model involving both agents. Based on such a model, the concept of a benchmark profile (BMP) - a two-dimensional analog of the single-dose BMD at which both agents achieve the specified BMR - is defined for use in quantitative risk characterization and assessment. The resulting, joint, low-dose guidelines can improve public health planning and risk regulation when dealing with low-level exposures to combinations of hazardous agents.

| S-EPMC3539281 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data