Bayesian Network Modeling Approach Research Articles

Bridging the Gap Between Human Toxicology and Ecotoxicology Under One Health Perspective by a Cross-Species Adverse Outcome Pathway Network for Reproductive Toxicity.

Although ecotoxicological and toxicological risk assessments are performed separately from each other, recent efforts have been made in both disciplines to reduce animal testing and develop predictive approaches instead, for example, via conserved molecular markers, and in vitro and in silico approaches. Among them, adverse outcome pathways (AOPs) have been proposed to facilitate the prediction of molecular toxic effects at larger biological scales. Thus, more toxicological data are used to inform on ecotoxicological risks and vice versa. An AOP has been previously developed to predict reproductive toxicity of silver nanoparticles via oxidative stress on the nematode Caenorhabditis elegans (AOPwiki ID 207). Following this previous study, our present study aims to extend the biologically plausible taxonomic domain of applicability (tDOA) of AOP 207. Various types of data, including in vitro human cells, in vivo, and molecular to individual, from previous studies have been collected and structured into a cross-species AOP network that can inform both human toxicology and ecotoxicology risk assessments. The first step was the collection and analysis of literature data to fit the AOP criteria and build a first AOP network. Then, key event relationships were assessed using a Bayesian network modeling approach, which gave more confidence in our overall AOP network. Finally, the biologically plausible tDOA was extended using in silico approaches (Genes-to-Pathways Species Conservation Analysis and Sequence Alignment to Predict Across Species Susceptibility), which led to the extrapolation of our AOP network across over 100 taxonomic groups. Our approach shows that various types of data can be integrated into an AOP framework, and thus facilitates access to knowledge and prediction of toxic mechanisms without the need for further animal testing. Environ Toxicol Chem 2024;00:1-14. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: A Bayesian Network Modeling Approach

Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.

Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

BackgroundMalaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.MethodsIn the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.ResultsModel accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).ConclusionRanking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Application of a Bayesian network modelling approach to predict the cascading effects of COVID-19 restrictions on the planting activities of smallholder farmers in Uganda

CONTEXTThere are rising concerns over the cascading effects induced by COVID-19 restrictions on the planting activities of smallholder farmers in low and middle-income countries, which may become a non-negligible threat to the long-term food security. Studies that utilize probability based models to examine the effects of COVID-19 restrictions on planting activities of smallholder farmers are scare, with no available evidence on Uganda. Yet these effects do not act in isolation, and are known to be complex, stochastic, nonlinear, and multidimensional. OBJECTIVETo develop a Bayesian network (BN) model based on expert knowledge, existing literature, and Uganda's High Frequency Phone Survey (HFPS) datasets on COVID-19 to bridge this gap. METHODSA comprehensive survey of relevant literature on the effects of COVID-19 restrictions on the planting activities of smallholder farmers was conducted based on well established guidelines. Resultantly, 23 relevant publications were obtained, and reviewed. A total of 12 variables deemed relevant to smallholder famers in Uganda were extracted, and organized into an influence diagram. The influence diagram was used to develop the BN model. A total 6313 households aggregated from Round 1, 4 and 7 of the HFPS datasets on COVID-19 was used in this study. A training portion (75%, n = 4734) was used to populate the model, and test dataset (25%, n = 1578), was used evaluate model accuracy. RESULTS AND CONCLUSIONSThe error rate was 17.9%% implying that the model had the majority of its predictions correct (82.1%). The model's classification power, was evaluated basing on the scoring rules. The model's scoring rule results indicated that the model has a strongest predictive power with both the logarithmic loss (0.45,) and quadratic loss (0.29) scores close to zero, while a spherical payoff (0.84) approaching 1. Results reveal the maize, beans, and ground nuts, were the most grown crops during the pandemic as compared to the period before the pandemic. The sensitivity results indicate that the probability of COVID-19 restrictions to affect the planting activities of the smallholder farmers in Uganda was 30%. The variables of ‘unable to acquire seeds, and fertilizers’ affected the planting activities by 2.6 percentage points (PP), and 1.3 PP respectively. The variables ‘travel restrictions’ and reduced labour, affected the planting activities by 11 PP and 1PP respectively. SIGNIFICANCEThese findings emphasize the importance of intervening on the highly ranked effects to enhance the resilience of local food systems, and smallholders' capacity to cope with recurring and unforeseen shocks.

Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach

PurposeThe supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context.Design/methodology/approachThis study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions.FindingsThe study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios.Originality/valueThis study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.

A Bayesian Network Modeling Approach for Time-Varying Flood Resilience Assessment of Housing Infrastructure System

Natural hazard causes severe types of damage to infrastructure systems at regular intervals, and the occurrence of such events is inevitable. The concept of resilience is adopted to make infrastructure systems more reliable, adaptable, and recoverable against natural disasters. Resilience is defined as the ability of an infrastructure to resist the impact of a disaster and bounce back to its desirable performance level after the disaster. Recovery of infrastructure, which forms a part of resiliency, is a time-dependent process in nature. In this work, a dynamic Bayesian network (BN) model is developed for the resilience assessment of housing infrastructure against flood hazards. The proposed resilience model is then implemented in the testbed of Barak Valley in North-East India. An extensive field survey is performed to collect relevant indicator data for resilience assessment and validation. To assess the recovery process, the housing infrastructure resiliency of the Barak Valley testbed is evaluated and compared between multiple flood event time periods. Lastly, the most critical indicators of the proposed dynamic BN model are identified by performing sensitivity analysis. This study will help the planner, designers, policymakers, and stakeholders to provide resilience-based decisions on flood resiliency of housing infrastructure systems.

Bayesian network modeling for economic-socio-cultural sustainability of neighborhood-level urban communities: Reflections from Kolkata, an Indian megacity

The study has adopted a knowledge-based, decision support system of Bayesian network (BN) modeling approach to address the lack of incorporation of the interlinkage of dimensions and uncertainty associated with neighborhood sustainability assessment (NSA). The study aims in developing a three-tier top-down BN model incorporating expert elicitation with three sub-models constituting 30 nodes concerning the Economic, Social, and Cultural Dimensions for the assessment of economic-sociocultural (ESC) sustainability of neighborhood-level urban communities (NLUCs) in Kolkata, an Indian megacity. The conceptual BN investigates the causal relationships and effects of different indicators and sub-models by quantifying their influence in achieving ESC sustainability. The study has proposed a robust algorithm for model parametrization and a detailed methodology for its structure development and validation. It has been tested using 550 sets of real-world survey data, with a prediction error rate of 2% for the query node. The model suggests the social sub-model is most sensitive to ESC sustainability, followed by the Economic and Cultural sub-models. It concludes with a discourse concerning spatial planning of NLUCs on dealing with the COVID-19 pandemic in compact cities of the Global South and discusses the relevance of the indicator set for achieving long-term sustainability.

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach.

Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field’s larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach

A continuous-variable Bayesian network (cBN) model is used to link watershed development and climate change to stream ecosystem indicators. A graphical model, reflecting our understanding of the connections between climate change, weather condition, loss of natural land cover, stream flow characteristics, and stream ecosystem indicators is used as the basis for selecting flow metrics for predicting macroinvertebrate-based indicators. Selected flow metrics were then linked to variables representing watershed development and climate change. We fit the model to data from two river basins in southeast US and the resulting model was used to simulate future stream ecological conditions using projected future climate and development scenarios. The three climate models predicted varying ecological condition trajectories, but similar worst-case ecological conditions. The established modeling approach couples mechanistic understanding with field data to develop predictions of management-relevant variables across a heterogeneous landscape. We discussed the transferability of the modeling approach.

Value of information of structural health monitoring with temporally dependent observations

A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

Temporal Bayesian Network modeling approach to evaluating the emotional cascade model of borderline personality disorder.

Theoretical models of personality disorders can be complex and multifaceted, making it difficult to validate such models in a comprehensive, empirical fashion. One such model of borderline personality disorder (BPD) is the emotional cascade model (Selby & Joiner, 2009), which has garnered empirical support in piecemeal fashion but has not been examined in a gestalt fashion. One way to test comprehensive models of personality pathology is with Temporal Bayesian Network (TBN) modeling, in which the relations between multiple subcomponents of a model can be specified and examined over a dynamic time frame, allowing for the modeling of positive feedback processes in addition to comprehensive model utility. In this study, we applied TBN modeling to examine the emotional cascade model in a sample of adolescents and young adults who actively self-injure, including those with BPD. TBN modeling was applied to ecological momentary assessment data provided via participant smartphone assessments for a period of 2 weeks. TBN analysis suggested that the emotional cascade model has considerable predictive utility, demonstrating substantial accuracy in predicting BPD diagnosis (with accuracy estimates around 90%) and momentary prediction of rumination, negative emotion, and dysregulated behaviors (with accuracy estimates consistently above 70% and reaching up to 100%, depending on the level of momentary prediction specificity). These findings provide support and validity to the notion that BPD may emerge from a dynamic interplay between emotional cascades and dysregulated behaviors. Implications of TBN modeling of BPD and personality disorders, in general, are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Influential factors on microplastics occurrence in river sediments

Rivers are regarded as sinks and pathways of plastic pollutants from terrestrial environments to various other aquatic systems such as lakes and oceans. Although extensive research has been conducted in recent years on microplastic pollution in river sediments, knowledge on the relationships between potential influential factors such as catchment characteristics and sediment properties, and microplastics occurrence in river sediments, is still considered an under-researched area. This study evaluated the influence of land use, population, and sediment particle size on the presence of microplastics abundance and types. Based on a Bayesian Network modelling approach to characterise the correlations between influencing factors and microplastics occurrence, it was evident that microplastics type had a positive correlation with different land use types and population. Catchment characteristics were found to play a more important role in influencing microplastics type than microplastics concentration. A statistically significant positive relationship was observed between microplastics concentration and clay particles which suggested that the occurrence of microplastics in sediments can be impacted by the presence of clay particles.

Supplemental Material for Temporal Bayesian Network Modeling Approach to Evaluating the Emotional Cascade Model of Borderline Personality Disorder

Supplemental Material for Temporal Bayesian Network Modeling Approach to Evaluating the Emotional Cascade Model of Borderline Personality Disorder

Understanding the Interplay of Lies, Violence, and Religious Values in Folktales

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character. A violent act committed to serving Buddhist mission results in a happy ending for the committer.

Vulnerability Assessment during Mass Evacuation: Integrated Microsimulation-Based Evacuation Modeling Approach

This study develops an integrated microsimulation-based evacuation model that performs a vulnerability assessment of the Halifax Peninsula, Canada during an evacuation. The proposed framework of vulnerability assessment accounts for long-term changes in neighborhood composition in relation to socio-demographic characteristics, residential locations, and vehicle ownership. The results of a large-scale urban systems model and a flood risk model are used to inform the vulnerability assessment. The urban systems model encapsulates long-term household decisions and life stage transitions in measuring social vulnerability. The flood risk model provides information on flood severity and finer network disruptions. In addition, a dynamic traffic assignment-based microsimulation model is developed to assess mobility vulnerability during an evacuation. One of the key contributions of this study is that it utilizes a Bayesian Belief Network modeling approach for vulnerability assessment, while addressing uncertainty and causal relationships between different elements of vulnerability. The results suggest that the Peninsula zones are at a relatively higher risk from a mobility point of view. A sensitivity analysis reveals that clearance time has been found to be the key determinant of the mobility vulnerability during an evacuation. “Presence of female” and “presence of seniors” are found as the two most significant contributors of social vulnerability. Several peripheral zones are at a higher risk because of their proximity to the flood source. The proposed research will help emergency professionals and engineers to develop effective evacuation plans in relation to vulnerable areas.

Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.

Loneliness of Older Adults: Social Network and the Living Environment.

The social participation and integration of older adults are important aspects of healthy aging. However, in general, older adults have smaller social networks than their younger counterparts due to changes in their life cycle stage, such as retirement or age-related losses, along with a declining health and increasing mobility limitations. Consequently, with increasing age, an increasing proportion of older people experience feelings of loneliness and social isolation. Previous studies that have analyzed the relationships between loneliness, social networks, and the living environment have often been based on bivariate relationships or included only a limited number of variables. Therefore, the aim of this study was to analyze multiple relationships in a more comprehensive framework. Data were collected using a survey among 182 adults aged 65 years and over in the Netherlands. A Bayesian belief network (BBN) modeling approach was used that derives all direct and indirect relationships between the variables. The results showed that feelings of loneliness are directly related to satisfaction with one’s social network and neighborhood attachment and are indirectly related to perceived safety and satisfaction with local amenities and services. This knowledge is relevant to urban planners and policy makers who focus on creating livable and healthy social neighborhoods for the aging population.

First use of participatory Bayesian modeling to study habitat management at multiple scales for biological pest control

Habitat management is increasingly considered as a promising approach to favor the ecosystem service of biological control by enhancing natural enemies. However, habitat management, whether at local or landscape scale, remains very uncertain for farmers. Interactions between ecological processes and agricultural practices are indeed uncertain and site-specific, which makes implementation difficult. Thus, prospecting innovations based on habitat management may benefit from integrating local stakeholders and their knowledge. Our objective is to explore with both local and scientific stakeholders how they perceive agricultural practices, ecological processes, and services related to biological pest control and habitat management. We conducted a participatory Bayesian Network modeling approach with five stakeholders in Southwest France around apple orchard cultivation. We co-constructed such Bayesian Networks based on participants’ knowledge. We explored scenarios favoring natural enemies and habitat manipulation with each participant’s Bayesian Network. We compared how different stakeholders perceive the impact of each scenario on the biological control ecosystem service. Our results indicate that a landscape with a high proportion of semi-natural habitats does not translate into significant biological control for most participants even though some stakeholders perceive a significant impact on generalist predators’ activity within orchards. For these local stakeholders, habitat management at the orchard level such as inter-row vegetation seems currently more promising than at the landscape scale. Here, we show for the first time that the use of Bayesian modeling in a participatory manner can give precious insights into the most promising perspectives on habitat management at different scales. These different local perspectives suggest in particular that further dialogue between ecologists and local stakeholders should be sought about inter-row habitat management as the most promising practice to foster biological pest control and other ecosystem services.

On How Religions Could Accidentally Incite Lies and Violence: Folktales As a Cultural Transmitter

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique data-set extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character. A violent act committed to serving Buddhist missions results in a happy ending for the committer. What is highlighted here is a glaring double standard in the interpretation and practice of the three teachings: the very virtuous outcomes being preached, whether that be compassion and meditation in Buddhism, societal order in Confucianism, or natural harmony in Taoism, appear to accommodate two universal vices — violence in Buddhism and lying in the latter two. These findings contribute to a host of studies aimed at making sense of contradictory human behaviors, adding the role of religious teachings in addition to cognition in belief maintenance and motivated reasoning in discounting counterargument.

Ecosystem service analysis in marginal agricultural lands: A case study in Belize

Globally, marginal lands, or less favored areas (LFAs), cover significant areas with large human populations, yet are relegated in policy making due to their perceived low agricultural value and a lack of information about other ecosystem services (ES) they may provide. Here we applied a simple, inclusive and qualitative ES inventory and Bayesian Belief Network modelling approach to a neo-tropical savanna LFA in Belize to assess its ES benefits, and potential trade-offs from future conversion to agriculture or a protected area. We found that consulting a broader selection of stakeholders elicited a more diverse range of ES, beyond the agricultural provisioning services considered in government planning. Further, the majority of the ES identified were accessed informally and so may be diminished under land use alternatives that formalize land tenure. We argue that, given the similar context of other LFAs, and the wider applicability of our technique, these findings have broader significance in the natural resource management and ES assessment field. Generally, we argue that simple qualitative ES analyses can efficiently provide useful planning information, and can assess how land use changes may impact local livelihoods. We argue that such methods can help improve natural resource management in LFAs and elsewhere.