Application Of Risk Assessment Research Articles

Framework and application of multi-criteria ship collision risk assessment

The paper proposes an integrated multi-criteria framework for assessing dynamically the ship collision risk under different scenarios. The framework identifies collision candidates, evaluates collision parameters and weights the importance of each collision parameter in different conditions, according to the ship officers’ experience. The information is aggregated using the evidential reasoning approach allowing real-time collision risk evaluations of collision scenarios. The framework is used to assess the collision risk in Portuguese continental coastal waters to illustrate its applicability. Moreover, the rationality of the proposed framework is assessed through a comparative analysis with other aggregating approaches such as the linear regression and the fuzzy regression models.

TRiceR: A Web Application for Supporting Risk Assessment of Ice Falling from Wind Turbine Blades

TRiceR: A Web Application for Supporting Risk Assessment of Ice Falling from Wind Turbine Blades

Creating a Nationwide Composite Hazard Index Using Empirically Based Threat Assessment Approaches Applied to Open Geospatial Data

The US is exposed to myriad natural hazards causing USD billions in damages and thousands of fatalities each year. Significant population and economic growth during the last several decades have resulted in more people residing in hazardous places. However, consistent national-scale hazard threat assessment techniques reflecting the state of hazard knowledge are not readily available for application in risk and vulnerability assessments. Mapping natural hazard threats is the crucial first step in identifying and implementing threat reduction or mitigation strategies. In this study, we demonstrate applied GIS approaches for creating and synthesizing US hazard threat extents using publicly available data for 15 natural hazards. Individually mapping each threat enables empirically supported intervention development and the building of a Composite Hazard Index (CHI). Summarizing the hazard frequencies provides a novel representation of US hazardousness. Implementing cluster analysis to regionalize US counties based on their underlying hazard characteristics offers insight into hazard threats’ spatial (non-political) natures. The results indicate that the southeast, central plains, and coastal regions of the northeast had high hazard occurrence scores, whereas more moderate hazard scores were observed west of the continental divide. Furthermore, while no place is safe from hazard occurrence, identifying each region’s distinct “hazardousness” can support individualized risk assessments and mitigation intervention development.

Agent‐based simulation of cross‐contamination of Escherichia coli O157:H7 on lettuce during processing and temperature fluctuations during storage in a produce facility. Part 2: Model implementation

AbstractAn agent‐based simulation was developed to represent the spatial and temporal Escherichia coli O157:H7 cross‐contamination dynamics in a processing facility for fresh‐cut romaine lettuces using NetLogo. An extension was added to the model to predict E. coli O157:H7 growth due to temperature abuses in a cold storage facility. A user‐friendly interface was created to follow variations in model outputs. The number of contaminated bags and lettuce contamination levels were computed and visualized. Experimental data of cross‐contamination from literature was used to describe the facility and validate the model. The key factor affecting cross‐contamination in the process is the free chlorine (FC) concentration dose rate. The number of contaminated bags per batch decreases nonlinearly as FC is added to the flume tank. The contamination level in the shredded lettuces increases linearly with initial microbial load or the initial contamination probability on lettuce heads. Both the level and probability of contamination in the facility's environment alter the number of contaminated bags. Batch size influences the number of contaminated bags when the first incoming lettuce batch is contaminated. The smaller the batch size the smaller number of contaminated bags. Storage room temperature fluctuations (13 and 19°C and 8 and 12°C) show the importance of real‐time monitoring to avoid pathogen growth and number of contaminated bags. This work provides insight on applications of agent‐based modeling approach using NetLogo to visualize cross‐contamination in fresh‐cut leafy green processing operations. It analyzes cross‐contamination and its impact on processing performance by studying the effect of mitigation strategies.Practical applicationsTo support ready‐to‐eat leafy greens postharvest operations, this work investigated the benefits of using visualization to develop strategies to prevent produce cross‐contamination along processing and the effect of temperature abuse during storage. Output from the model can be used in Quantitative Management Risk Assessment (QMRA) applications. By developing a simulation‐based decision support system, the safety of processing and storage steps of fresh‐cut leafy greens are investigated. Pathogen cross‐contamination and growth models are integrated to observe impacts of varying contamination levels in incoming produce and equipment surfaces, concentrations of free chlorine in the wash water, and storage temperatures on the number of contamination bags over time. Computational experiments study a pilot plant of Romaine lettuces contaminated with E. coli O157:H7. Results indicate that the key factor affecting cross‐contamination is the chlorination concentration dose rate in the wash water. Other factors include the level of contamination on the incoming lettuce heads or in the facility environment (equipment). Storage room temperature fluctuations showed the importance of real‐time monitoring to avoid microorganism growth and thus prevent an increase in the number of contaminated bags.

Allergic contact dermatitis caused by 2-hydroxyethyl methacrylate and ethyl cyanoacrylate contained in cosmetic glues among hairdressers and beauticians who perform nail treatments and eyelash extension as well as hair extension applications: A systematic review.

Current cosmetic regulations primarily focus on protecting consumers, not the professional user who is subjected to a partly different, and certainly more intense exposure to hazardous substances. Against this background, this systematic review aims to compile and appraise evidence regarding skin toxicity of 2-hydroxyethyl methacrylate (HEMA; CAS no. 212-782-2) and ethyl cyanoacrylate (ECA; CAS no. 7085-85-0) contained in cosmetic glues used among hairdressers and beauticians who perform nail treatments and eyelash extension as well as hair extension applications. This systematic review followed the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) 2020 recommendations for reporting systematic reviews and meta-analysis. In total, six publications from six countries were eligible for this systematic review. A meta-analysis revealed that hairdressers and beauticians have a ninefold increased risk of developing contact allergy to HEMA compared with controls who are not hairdressers and beauticians. Results for ECA are lacking. The results of this systematic review clearly show that-regarding contact allergy to acrylates-it is not appropriate to apply risk assessment for consumers to hairdressers and beauticians who occupationally handle cosmetic glues. The regulations in existence do not adequately address occupational risks for hairdressers and beauticians connected with the use of acrylate-containing cosmetic substances and need reconsideration.

Aortic Pulse Wave Velocity as Adjunct Risk Marker for Assessing Cardiovascular Disease Risk: Prospective Study.

Aortic pulse wave velocity is a noninvasive measure of aortic stiffness and arterial aging. Its current value in cardiovascular risk estimation practice is unknown. We aimed to establish whether aortic pulse wave velocity identified individuals with higher risk of incident major adverse cardiovascular events and improved performance of the American Heart Association/American College of Cardiology atherosclerotic cardiovascular disease risk score. This prospective analysis included 3837 Whitehall II cohort participants screened in 2008 to 2009, and followed for 11.7 years (mean=10.3, SD=1.81), without history of stroke, myocardial infarction, or coronary heart disease. Mean age of the sample was 65.0 years (SD=5.6), 2831 participants (73.8%) were male and mean atherosclerotic cardiovascular disease risk score was 13.8%. At the end of follow-up, 411 individuals (10.7%) had suffered a major cardiovascular event. Those in the highest aortic pulse wave velocity quartile were at high risk (hazard ratio, 2.99 [95% CI, 2.25-3.97]) and reached the threshold for statin medication (7.5% risk) after 5 years whereas others reached it after 10 years (difference P<0.001). The addition of aortic pulse wave velocity to the risk score improved the C statistic (0.68 versus 0.67, P=0.03) and net reclassification index (4.6%, P=0.04 and 11.3%, P=0.02). Our results show that aortic stiffness predicted major adverse cardiovascular events in a cohort of elderly individuals, improving the performance of a widely used cardiovascular disease risk estimator. Aortic pulse wave velocity measurement is scalable, radiation-free, and easy to perform. Further studies on its applicability in cardiovascular disease risk assessment in primary care settings are needed.

Analisis Risiko Proyek Pengembangan Perangkat Lunak Menggunakan Kerangka Kerja ISO 31000

PT. Duta Media Cipta (DMC) is a control system and IT company based in the city of Surabaya, this company focuses on procurement, maintenance and repair services for ship equipment, both mechanical and electrical. As an information technology company, PT. DMC is committed to being a trusted IT and control system company. In working on the project, PT. DMC has not implemented risk management for every software development project it has worked on. Some of the risks that are often faced in working on software projects at PT. DMC are project risks such as technical, cost, and project scheduling which are sometimes not in accordance with the established plan and cause cost and time losses to the company. So in this study using ISO 31000 as a framework in analyzing potential risks. The application of risk assessment consists of 3 stages, namely risk identification, risk analysis, and risk evaluation. The results of this study found that there are 4 risks with 26 sub-risks, of which there are 6 sub-risks that are prioritized and require risk management.

Vicia-Micronucleus Test Application for Saline Irrigation Water Risk Assessment.

In view of climate change, increasing soil salinity is expected worldwide. It is therefore important to improve prediction ability of plant salinity effects. For this purpose, brackish/saline irrigation water from two areas in central and coastal Tunisia was sampled. The water samples were classified as C3 (EC: 2.01–2.24 dS m−1) and C4 (EC: 3.46–7.00 dS m−1), indicating that the water was questionable and not suitable for irrigation, respectively. The water samples were tested for their genotoxic potential and growth effects on Vicia faba seedlings. Results showed a decrease in mitotic index (MI) and, consequently, growth parameters concomitant to the appearance of micronucleus (MCN) and chromosome aberrations when the water salinity increased. Salt ion concentration had striking influence on genome stability and growth parameters. Pearson correlation underlined the negative connection between most ions in the water inappropriate for irrigation (C4) and MI as well as growth parameters. MI was strongly influenced by Mg2+, Na+, Cl−, and to a less degree Ca2+, K+, and SO42−. Growth parameters were moderately to weakly affected by K+ and Ca2+, respectively. Re-garding MCN, a very strong positive correlation was found for MCN and K+. Despite its short-term application, the Vicia-MCN Test showed a real ability to predict toxicity induced by salt ions confirming that is has a relevant role in hazard identification and risk assessment of salinity effects.

Roadscene2vec: A tool for extracting and embedding road scene-graphs

Recently, road scene-graph representations used in conjunction with graph learning techniques have been shown to outperform state-of-the-art deep learning techniques in tasks including action classification, risk assessment, and collision prediction. To enable the exploration of applications of road scene-graph representations, we introduce roadscene2vec: an open-source tool for extracting and embedding road scene-graphs. The goal of roadscene2vec is to enable research into the applications and capabilities of road scene-graphs by providing tools for generating scene-graphs, graph learning models to create spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing scene-graph-based methodologies. The capabilities of roadscene2vec include (i) customized scene-graph generation from either video clips or data from the CARLA simulator, (ii) multiple configurable spatio-temporal graph embedding models and baseline CNN-based models, (iii) built-in functionality for using graph and sequence embeddings for risk assessment and collision prediction applications, (iv) tools for evaluating transfer learning, and (v) utilities for visualizing scene-graphs and analyzing the explainability of graph learning models. We demonstrate the utility of roadscene2vec for these use cases with experimental results and qualitative evaluations for both graph learning models and CNN-based models. roadscene2vec is available at https://github.com/AICPS/roadscene2vec.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement.

BackgroundAs the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety.MethodsQualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee.ResultsThe literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020.ConclusionsThe findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Screening and prioritization of nano- and microplastic particle toxicity studies for evaluating human health risks – development and application of a toxicity study assessment tool

Concern regarding the human health implications that exposure to nano- and microplastic particles (NMPs) potentially represents is increasing. While there have been several years of research reporting on the ecotoxicological effects of NMPs, human health toxicology studies have only recently emerged. The available human health hazard data are thus limited, with potential concern regarding the relevance and reliability for understanding the potential human health implications. In this study we develop and apply a NMP toxicity screening assessment tool (NMP-TSAT) for evaluating human health effects studies against a suite of quality assurance and quality control (QA/QC) criteria for both in vivo and in vitro studies. A total of 74 studies representing either inhalation or oral exposure pathways were identified and evaluated. Assessment categories include particle characterization, experimental design, and applicability for risk assessment; with critical and non-critical criteria organized to allow screening and prioritization. It is observed that the majority of studies evaluated using the NMP-TSAT have been performed on monodisperse particles, predominately spheres (≈60%), consisting of polystyrene (≈46%). The majority of studies have tested particles < 5 μm, with a minimal particle size of 10 nm and a maximum particle size of about 200 μm. The total assessment score (TAS) possible for in vivo studies is 52, whereas for in vitro studies it is 46, which is based on receiving a maximum score of 2 against 26 and 23 criteria, respectively. The evaluated TAS ranged from between 12 and 44 and 16–34, for in vivo and in vitro studies, respectively. Given the challenges associated with prioritizing studies based on ranking them according to their TAS we propose a Tiered approach, whereby studies are initially screened based on how they score against various critical criteria, which have been defined for their relevance for assessing the hazards and risks for human health. In this instance, studies that score a minimum of ‘1’ against each of the critical criteria, regardless of how they rank according to their TAS, are prioritized as part of a Tier 1 screening and prioritization phase, which would then be followed by an expert evaluation, representing a Tier 2 level of assessment. Using this approach we identify 10 oral ingestion and 2 inhalation studies that score at least 1 against all critical criteria. Lastly, several key observations for strengthening future effects studies are identified, these include a need for the generation and access to standard reference materials representative of human exposure to NMPs for use in toxicity test systems and/or the improved characterization and verification of test particle characteristics, and the adoption of study design guidance, such as recommended by OECD, when conducting either in vivo inhalation or oral ingestion toxicity tests.

The Perks and Drawbacks of Physiologically-Based Pharmacokinetic Modeling

Physiologically-based pharmacokinetic (PBPK) models use a mechanistic approach to integrate physiological parameters, physicochemical drug properties, and biochemical processes by means of mathematical equations to predict the concentration of a drug over time in blood and tissues. These models represent a robust tool for the pharmaceutical sciences, but also applications in chemical risk assessment for drugs and chemicals of forensic interest or in occupational hazards have been explored over the last decade. Much like other computational tools, PBPK models’ application face challenges concerning validity, transparency and lack of collaboration between professionals in different fields. The potential uses and challenges of PBPK modeling are discussed, as well as some ways to address the latter.

Research on Combining Pattern Mining and Evolutionary Algorithm for Critical Node Detection Problems

The critical node detection problems (CNDPs) have important applications in network security, smart grid, epidemic control, drug design, and risk assessment. The critical node problem (CNP) is one of well-known CNDPs, which is NP-hard. In this work, we combine frequent pattern mining with evolutionary algorithm for solving CNP, where pattern mined from high-quality solutions are used to guide the construction of offspring solution. More specifically, based on the memetic algorithm (Memetic Algorithm for CNP, MACNP), frequent pattern mining was integrated into the memetic algorithm framework. The frequent patterns mined were used to construct the offspring solution, instead of crossover operator in MACNP. In the research, the nodes in frequent items set were directly fixed as part of the offspring solution. So the offspring solution inherited excellent properties from more parent solutions, thus forming the algorithm called Frequent Pattern Based Search for CNP (FPBS-CNP). This strategy improved the algorithm efficiency. Experiments were conducted on synthetic and real-world benchmark. We experimentally compare FPBS-CNP with MACNP. The experiment results show that FPBS-CNP performs well on small instances and has potential in solving large instances.

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

Two novel bioassays useful for the quick assessment of chemical effects on the behavior of mosquito larvae (Culicidae) and adult earthworms (Lumbricidae)

The behavior, i.e., observable response of an organism can serve as a useful indicator of environmental quality. In the absence of death and other sublethal effects, behavioral changes can represent early warning signs of toxic effects. In the present contribution, we present two novel bioassays useful for the quick assessment of chemical effects on the behavior of mosquito larvae (Culicidae) and adult earthworms (Lumbricidae). These bioassays could have applications in ecological risk assessment and laboratory based ecotoxicological testing. The novel assays are:•A bioassay for the assessment of chemical toxicity on the swimming, breathing and resting behavior of Culicidae larvae.•A bioassay for the assessment of chemical toxicity on the escape behavior of Lumbricidae in water.•These bioassays were carried out using the systemic insecticide imidacloprid and documented the relatively rapid onset of the neurotoxic effects of imidacloprid to experimental organisms.

Application of hazard identification and risk assessment for reducing the occupational accidents in firework industries - specific reference to Sivakasi

Application of hazard identification and risk assessment for reducing the occupational accidents in firework industries - specific reference to Sivakasi

Application of hazard identification and risk assessment for reducing the occupational accidents in firework industries - specific reference to Sivakasi

Application of hazard identification and risk assessment for reducing the occupational accidents in firework industries - specific reference to Sivakasi

Water reuse in Brazilian rice farming: Application of semiquantitative microbiological risk assessment

The current paper presented the Semiquantitative model for microbiological risk assessment for human health, in rice crop production in Brazil. For this purpose, initially, Microbiological Risk Assessment (SqMRA) was divided into four stages: 1) Identification of hazards; 2) Identification of exposure routes for different receptors; 3) Exposure scenarization; 4) Risk characterization. After that, the SqMRA was applied to rice farming in two scenarios, in which the first considers the effluent to be disinfected (Hazard 7), and the second considers the reality of sewage treatment conditions in Brazil (Hazard 9). Thus, it was observed that the reuse of water reuse can be applied in rice farming, with an acceptable global risk, to the receptors involved (farmer, consumer, and neighborhood). Although, it is necessary the relevance of disinfection to minimize the risk in any water reuse application is highlighted.

Modeling Risk Dynamics of Contaminants of Emerging Concern in a Temperate-region Wastewater Effluent-dominated Stream.

Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/ spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions (i.e., including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems (i.e., algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs (i.e., RQ≥0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k<0.1 h-1) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.

A minimum spanning tree analysis of the Polish stock market

Aim/purpose – This article aims to explore the network topology of the stock market in Poland during the COVID-19 pandemic. Design/methodology/approach – Kruskal’s algorithm was used to find the minimum spanning trees (MST) of three undirected correlation networks: MST1 (December 2019 – August 2021), MST2 (February 2020 – April 2020), and MST3 (June 2021 – August 2021). There were123 firms included in all three networks representing three key indexes (WIG20, mWIG40, and sWIG80). Findings – The comovements of stock prices varied between various periods of the pandemic. The most central firms in Poland were PEO, UNT, SPL, PKO, KGH, CCC, and PZU. WIG20 was the most influential stock index for all networks. During the tur- bulent period represented by MST2, many of Poland’s largest companies have clustered around KGH at the center of the network. In contrast, MST3 is the least compact of the three networks and is characterized by the absence of a single strongly influential node. Research implications/limitations – Correlation networks are efficient at quantitatively describing the degree of interdependence of a stock. MST finding algorithms are a cru- cial method of analysis for correlation networks. However, a limitation of the study, inherent to undirected correlation networks, is the inability to determine the direction of influence that stocks have on each other. Originality/value/contribution – The results of the article contribute to the economic analysis of stock markets in several ways. First, it expands on Gałązka (2011) by includ- ing additional centralities and the dynamic aspect of changes in the topology during the COVID-19 pandemic. Second, it broadens the MST-based empirical research of stock markets by showing the emergence of the star topology during the period of high uncer- tainty in Poland. Third, it has practical applications for systemic risk assessment and portfolio diversification. Keywords: network analysis, minimum spanning tree, correlation network, stock mar- ket, COVID-19, Poland. JEL Classification: D85, L14, G10, G32.