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Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of N = 7797 $N=7797$ participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies during pandemics., (© 2024 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

Abstract Dhole (Cuon alpinus) is threatened with extinction across its range due to habitat loss and prey depletion. Despite this, no previous study has investigated the distribution and threat of the species at a regional scale. This lack of knowledge continues to impede conservation planning for the species. Here we modeled suitable habitat using presence‐only camera trap data for dhole and dhole prey species in mainland Southeast Asia and assessed the threat level to dhole in this region using an expert‐informed Bayesian Belief Network. We integrated prior information to identify dhole habitat strongholds that could support populations over the next 50 years. Our habitat suitability model identified forest cover and prey availability as the most influential factors affecting dhole occurrence. Similarly, our threat model predicted that forest loss and prey depletion were the greatest threats, followed by local hunting, non‐timber forest product collection, and domestic dog incursion into the forest. These threats require proactive resource management, strong legal protection, and cross‐sector collaboration. We predicted

Sufficient research on cultural ecosystem services (CESs) helps decision makers realize developmental and environmental goals in urban planning, budget allocation, and municipal service provision. The Bayesian belief network (BBN), as a semi-quantitative model, can be used to infer the supply and demand match for CESs. Taking Shenzhen as the study area, we evaluated the CES supply and demand. We used a BBN to simulate the CES supply and demand and identify the key influencing factors affecting the CES budget. CES budget expectations were created to indicate the CES supply and demand balance. The main conclusions are as follows. (1) Considering the community as the evaluation unit, the satisfaction rate of CES demand was 62.5% in Shenzhen. (2) Communities with a high possibility of a CES deficit are primarily distributed in Futian, Nanshan, Luohu, and Bao’an Districts, accounting for 7.45% of the area of Shenzhen. (3) The population density, CES supply potential, and CES supply opportunity jointly determined the CES supply and demand balance. (4) The CES budget expectation results demonstrate that communities with a high risk of imbalance are widely distributed, and balanced communities are primarily concentrated in the Guangming, Pingshan, and Dapeng New Areas. (5) It is necessary to enhance the fairness of CES supply in Shenzhen through the promotion of green space, improvement of infrastructure, and interconnection of communities. By combining BBN with CES as an exercise, this study aims to enrich the methods for evaluating the CES supply and demand balance, providing scientific suggestions for Shenzhen to build a livable city and achieve the Sustainable Development Goals.

• Large amounts of data are usually stored on different network nodes, which can lead to malicious data security and privacy issues. • This research a modified teaching-learning with fish swarm optimization (MTL-FS) technique to classify the abnormal network users as five classes are susceptive, contagious, doubt, immune and recoverable users. • Finally, the performance of proposed HSBD-PR approach evaluate by standard benchmark datasets is Enron email, Epinions and Facebook. • The results of proposed method are compared with the existing state-of-art methods in terms of different performance metrics are detection failure rate, detection success rate, marginal detection gain and detection accuracy respectively. In recent years, Online Social Networks (OSN) have become an integral part of human society. OSN has become an important and crucial mode of interaction among people around the world. For the past two decades, the need for data privacy security among the participants of an OSN has been challenged as technological advances have provided a lot of benefits and exposure to all active members sharing content in various online communities. Large amounts of data are usually stored on different network nodes, which can lead to malicious data security and privacy issues. When OSN users share any private content with the other participants of same network like with their friends and colleagues, their information may be inadvertently leaked to various other individuals to whom it is not supposed to be shared. Those individuals may include one's enemies, social robots, and users with fake credentials, spammers or data cutters. In addition, the spread of malicious through OSN can lead to massive damage to the network and privacy leakage. To overcome this problem, we propose a hybrid soft computing with big data analytics based protection and recovery strategy (HSBD-PR) for security enhancement in large scale OSNs. First, we introduce a modified teaching-learning with fish swarm optimization (MTL-FS) technique to classify the abnormal network users as five classes are susceptive, contagious, doubt, immune and recoverable users. Second, we fuse a block window optimization with Bayesian belief network (BWB2) for analyze the propagation mechanism of abnormal users in network. After that, deep belief neural network (DBNN) based protection and recovery strategy is developed to improve the immunity of abnormal users and reduce the count of it. Finally, the performance of proposed HSBD-PR approach evaluate by standard benchmark datasets is Enron email, Epinions and Facebook. The results of this proposed method are compared with the existing state-of-art methods in terms of different performance metrics are detection failure rate, detection success rate, marginal detection gain and detection accuracy respectively. [ABSTRACT FROM AUTHOR]

Ventricular extrasystoles (VE) are considered the most dangerous type of heart rhythm disorders for human life, their timely detection, diagnosis and prevention are urgent issues of cardiology. In order to ensure the objectivity of diagnosis of VE, it is necessary to process a large amount of information related to the results of various medical studies, tests, anamnesis, accompanying diseases, etc., along with a long-term Holter ECG monitor. In order to process such a large amount of information and make a correct diagnosis, the issue of applying medical expert systems (MES) to doctors is currently relevant. ESs using probabilistic models based on Bayes' theorem are currently preferred because there are uncertainties in medical diagnosis issues that the same symptoms may be related to different diseases. The object of this study is the development and construction of a Bayesian belief network (BBN) for the purpose of diagnosing VEs. The choice of BBN is justified by the fact that they have the ability to combine several types of information, as well as the ability to manage uncertainties and work with incomplete information. The result of the application of the developed BBN is a probabilistic assessment of the diagnosis of VE. This network was built in the NETICA system from Norsys Software Corp. A distinctive feature of this work is that when compiling the table of conditional probabilities of BBN for the diagnosis of VE, together with the results of ECG and echo-ECG studies, data on the influence of additional factors that play a role in the occurrence of VE were used, such as the index of oxygen saturation of erythrocytes in the blood, changes in the thickness of the intima-media layer of the aortic artery and the amount of lipid fractions of blood plasma. [ABSTRACT FROM AUTHOR]

The complexity of the composition and the increase in the number of technical systems lead to an increase in the intensity of their failures. As a result, there is a need to repair the equipment of complex technical systems, leading to system downtime. The search for failed components and the elimination of their failures contributes to an increase in the safety level of operation of complex technical systems. Diagnostics and prediction of failures of components of automated systems and mechanisms (subsystems, elements, intersystem and interelement connections) in real operation to find and eliminate the causes of failures remains an urgent task. The operational reliability of restored complex technical systems and their components is effectively achieved by the strategy of operating systems with technical condition monitoring based on technical diagnostic systems. Reducing failures and man-made risks in the operation of complex technical systems is facilitated by predicting their technical condition based on diagnostics. The article presents an intelligent system that operates using the developed model for assessing and predicting the risk of failure of components of a complex technical system using the example of a ship power plant. Building a model taking into account the hierarchical levels of subsystems (components), intersystem (interelement) connections of an intelligent system is based on the use of a priori information about failures of components of complex technical systems. The model connects the types of technical condition of components and diagnostic features of systems in the form of the risk of their failures. The use of a posteriori inference in Bayesian belief networks makes it possible to determine the risk of system component failures, taking into account the incoming diagnostic information and information about component failures. In order to build and research a diagnostic Bayesian network model of an intelligent system for assessing the risk of failures for a system for diagnosing and predicting the technical condition of the components of a complex technical system consisting of numerous variables, the software product GeNIe was used. The results of studies of the model for assessing and predicting the risk of failure of components of a complex technical system confirmed the possibility of predicting the risk of failure of components and the system as a whole. [ABSTRACT FROM AUTHOR]

Actor-level data on large-scale commercial agriculture in Sub-Saharan Africa are scarce. The peculiar choice of transnational investing in African land has, therefore, been subject to conjecture. Addressing this gap, we reconstructed the underlying logics of investment location choices in a Bayesian network, using firm- and actor-level interview and spatial data from 37 transnational agriculture and forestry investments across 121 sites in Mozambique, Zambia, Tanzania, and Ethiopia. We distinguish four investment locations across gradients of resource frontiers and agglomeration economies to derive the preferred locations of different investors with varied skillsets and market reach (i.e., track record). In contrast to newcomers, investors with extensive track records are more likely to expand the land use frontier, but they are also likely to survive the high transaction costs of the pre-commercial frontier. We highlight key comparative advantages of Southern and Eastern African frontiers and map the most probable categories of investment locations. [ABSTRACT FROM AUTHOR]

This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this analysis is to predict worsening post‐treatment normal tissue toxicity in patients undergoing accelerated partial breast irradiation (APBI) therapy and to quantitatively identify which diagnostic, anatomical, and dosimetric features are contributing to these outcomes. Methods: A retrospective study of APBI treatments was performed using 32 features pertaining to various stages of the patient's treatment journey. These features were used to inform and construct a Bayesian network (BN) based on both statistical analysis of feature distributions and relative clinical importance. The target feature for prediction was defined as a measurable worsening of telangiectasia, subcutaneous tissue induration, or fibrosis when compared against the observed baseline. Parameter learning for the network was performed using data from the 299 patients included in the ACCEL trial and predictive performance was measured. Feature importance for the BN was quantified using a novel information‐theoretic approach. Results: Cross‐validated performance of the BN for predicting toxicity was consistently higher when compared against conventional machine learning (ML) techniques. The measured BN receiver operating characteristic area under the curve was 0.960±$\,{\pm}\,$0.013 against the best ML result of 0.942±$\,{\pm}\,$0.021 using five‐fold cross‐validation with separate test data across 100 trials. The volume of the clinical target volume, gross target volume, and baseline toxicity measurements were found to have the highest feature importance and mutual dependence with normal tissue toxicity in the network, representing the strongest contribution to patient outcomes. Conclusions: The BN outperformed conventional ML techniques in predicting tissue toxicity outcomes and provided deeper insight into which features are contributing to these outcomes. [ABSTRACT FROM AUTHOR]

Financial, economic, and political risks pose a significant threat to the development and progress of countries as such risks can impact all spheres of life including education, healthcare, logistics, transportation, and safety among others. Although these risks seem quite distinct, they are mutually influenced by multidimensional interdependent factors such as internal and external conflict, socioeconomic conditions, corruption, law and order, and bureaucratic quality among others. In this paper, we utilize a data-driven approach to explore dependencies among factors influencing financial, economic, and political risks and establish their relative importance in a network setting while capturing the entire distribution of individual factors. A probabilistic network-based model was developed using the data by the International Country Risk Guide, which revealed significant differences between the conventional and the proposed schemes for prioritizing drivers of political, economic, and financial risks. Internal conflict and socioeconomic conditions were considered as the most critical factors in terms of reducing and enhancing the network-wide risk exposure, respectively. The two prioritization schemes relative to the vulnerability and resilience impact of individual factors are not correlated and therefore, policy-makers need to focus on both schemes while developing risk mitigation strategies. [ABSTRACT FROM AUTHOR]

An ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies. [ABSTRACT FROM AUTHOR]

Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases. Bayesian Belief Networks (BBN) are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity. The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling. The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment. Initial analysis from well workover data, of a 5-node BBN provided insights on two different levels of severity of an accident, the ’Accident’ and ’Incident’ level, and on its respective profiles of the initiating events and the investigation-reported human causes. The initial results demonstrate that the data learnt from the database can be used to structure the BBN, give insights on how human reliability pertaining to well activities can be modelled, and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes. It is also proposed that the integrated treatment of various sources of information (database and expert judgement) through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.

• A risk assessment model is proposed for maritime transportation. • The ER-based SLIM approach and Leaky Noisy-OR gate model are used in BBN analysis. • A risk assessment of a cargo tank fire/explosion on a chemical tanker is performed to illustrate the proposed model. • Critical factors contributing to cargo tank fire/explosion on chemical tanker are identified. The transportation of hazardous cargoes, particularly in chemical tankers, represents significant risks such as fire and explosion, which can have catastrophic consequences for both human life, commodity and maritime environment. This article presents a conceptual risk modelling for cargo tank fire/explosion incidents in chemical tanker ships under the Evidential Reasoning (ER) - Success likelihood index method (SLIM) approach with the Bayesian belief network (BBN) to provide a practical tool for assessing and managing the associated risks. Whilst the ER-SLIM approach offers a systematic human error probability (HEP) prediction and handling uncertainty in risk, allowing for the incorporation of expert judgments and data-driven information, the BBN provides a probabilistic graphical model to represent the causal relationships among various risk factors. The result shows that the risk of cargo tank fire/explosion during the cargo tank cleaning operation is calculated as 2.83E-02. The findings of the research including consequences analysis provide valuable insights for decision-makers, safety managers, superintendents, ship masters and officers in the maritime industry to prioritize risk management strategies and enhance the safety of chemical tanker operations. [ABSTRACT FROM AUTHOR]

Spatially explicit priority optimization based on the tradeoffs and synergies between multiple ecosystem services (ESs) is greatly responsible for improving sustainable land use management and human well-being in ecologically fragile regions (EFRs). Here, Inner Mongolia, a typical EFR in China, was taken as the study area. Its five prominent ESs, i.e., soil retention (SR), carbon storage (CS), habitat quality (HQ), water yield (WY), and windbreak and sand-fixing (WS), were firstly evaluated. The local bivariate Moran's I and the sensitivity analysis were adopted to identify the spatial relationships between them, and the key social-ecological variables affecting ESs, respectively. To simulate the spatially explicit priority optimization areas, four scenarios were designed using the Bayesian belief network. Results showed the five ESs had heterogeneous spatial distributions and temporal dynamics. Variations in relationships between paired ESs were found across space and time. Regional factors, including both natural and human influence, influenced the ESs. The spatially explicit priority optimization areas for forest and grassland were showed in different areas by the scenario analysis. Besides, diverse sustainable land use policies from the perspectives of protection, planning, and management were also suggested. These findings could provide valuable references for EFR sustainable development worldwide. • Land ecosystem services showed heterogeneity in Inner Mongolia in 2000–2020. • Regional human-natural factors play crucial roles in land ecosystem services. • Spatially explicit priority optimizations were simulated by using Bayesian network. • Corresponding strategies for improving land ecosystem services were suggested. [ABSTRACT FROM AUTHOR]

This study investigates the dependencies between the Global Food Security Index (GFSI) and indicators related to sustainability and adaptation using Bayesian Belief Network (BBN) models. Methodologically, the study utilizes the forward and backward propagation capabilities of BBNs in GeNIe software to identify critical indicators. It also examines the predictive power of these indicators by analyzing their mutual value of information in Hugin software. Data utilized in the analysis include GFSI data for 2022 and associated sustainability and adaptation indicators. Key findings reveal the critical importance of proactive risk management strategies, sustainable agricultural practices, and effective governance mechanisms in shaping food security outcomes. Disparities between high- and low-performing countries in GFSI outcomes underscore the need for tailored interventions to address systemic vulnerabilities. High-performing countries in the food security environment exhibit a very high probability of 93% of achieving high performance in the national agricultural adaptation policy. Conversely, low-performing countries exhibit a very high probability of 91% of achieving low performance in the agricultural water risk – quality indicator. The study contributes theoretically by uncovering the complex interactions within food systems and practically by offering insights for policymakers and practitioners to develop targeted interventions. The implications of the findings emphasize the importance of integrated approaches and evidence-based policymaking to address food security challenges effectively. • Investigates Global Food Security Index (GFSI) dependencies with sustainability and adaptation. • Employs BBN models to analyze direct indicator connections. • Utilizes 2022 GFSI data and associated indicators. • Emphasizes proactive risk management and governance. • Reveals disparities, guiding tailored interventions. [ABSTRACT FROM AUTHOR]

• A risk assessment framework is developed to assess inland navigational risk of remotely controlled autonomous ship. • A novel approach is proposed for generating Conditional Probability Tables. • A two-dimensional diagram is designed to visualize the likelihood and consequence. • The Bayesian Belief Network model is quantitatively verified using several approaches. The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident's probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers. [ABSTRACT FROM AUTHOR]

• WY, CS and HQ in the Yellow River Basin have a synergistic connection. • SR is in a trade-off relationship with WY, CS and HQ in the Yellow River Basin. • NDVI, temperature, precipitation, and LU were the main variable that affect ESs. • LU is the main driving factor of this trade-off and synergy relationship. A systematic understanding of shifts in the temporal and spatial distributions of ecosystem services (ESs) in the Yellow River Basin and the trade-offs and synergies between these services and their driving variables is essential for effective management of the environment and for enhancing human well-being. This study quantitatively assessed the carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, combinded Bayesian Belief Network (BBN) and ESs, determined the crucial elements influencing the supply of ESs via sensitivity analysis, identified synergies and trade-offs between ESs via probabilistic reasoning, and explored the driving processes via scenario analysis. The following are the key findings of this research. (1) Geographically, the Yellow River Basin, SR, CS, and WY exhibited higher levels in southern regions and lower levels in northern regions, whereas HQ showed higher levels in the northwestern region and lower levels in the southeastern region. Both WY and CS showed annual increases in the time series. (2) With respect to trade-offs and synergistic interactions, HQ, WY, and CS had a synergistic connection, whereas SR and HQ, CS, WY had a trade-off connection. (3) According to the BBN sensitivity analysis, the normalized difference vegetation index (NDVI), temperature, precipitation, and land use (LU) were the main variable nodes affecting ESs. (4) The results of the scenario analysis revealed that LU was the primary driver of the four ESs In addition, precipitation and NDVI primarily drove the synergistic and trade-off relationships between HQ, CS, and WY. This study combined BBN and ESs to develop a new method for investigating ES functions and established a solid scientific foundation for ES management and macroeconomic growth planning. [ABSTRACT FROM AUTHOR]

Maritime autonomous surface ships (MASS) are one of the hot topics in maritime transportation even though they bring many challenges in terms of safety, security, and environment. This paper tackles the safety-related challenges of remotely controlled ships without seafarers on board but controlled at the shore. In this context, the reliability of the operator is of paramount importance for safe and efficient MASS operation. This paper performs systematic human reliability analysis for the operator of MASS (for degree 3) under the Bayesian belief network (BBN) and evidential reasoning (ER)- cognitive reliability and error analysis method (CREAM) approach. In the model, BBN is capable of determining the probability distribution of Contextual Control Modes in CREAM, while ER tackles the uncertainty and subjectivity of expert judgments. The outcome of the paper shows that the human reliability for remote control MASS operation is 8.88E-01. Besides its robust theoretical background, the paper will provide the utmost contributions to operators, managers, safety inspectors, and ship owners of MASS for safer and reliable operations. [ABSTRACT FROM AUTHOR]

Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia's Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents. [ABSTRACT FROM AUTHOR]

The traditional audit process in construction cost control usually occurs passively at the end of a project life cycle. This calls for a predictive model that provides a framework assembling essential information and predicts construction cost overrun potential during project processes. Unlike previous mechanistic models that reflect the full formation mechanism, the model established in this paper features a fragmentary formation mechanism consisting of shortlisted critical factors. Factors were shortlisted by both theoretical and statistical criticality to construction cost overrun, dictating the factors to pass the initial literature review identification, expert opinion verification, and Pearson's chi-square tests of interdependence. The factor shortlist was compared with the initial long list identified from the literature to understand the longitudinal trend. The trend manifested in this research necessitated a shift of project management focus from technical difficulties to managerial issues, signaled by the shifting emphasis from contractor planning and control to client monitoring and management and from project difficulties to contract qualities. The shortlisted factors and their interrelationships together formed a fragmentary mechanism and gave the model structure, which was quantified with Bayesian belief network analysis. The model automatically can calculate cost overrun potentials with relevant input and use influence diagrams to find optimal decisions maximizing the expected values of construction cost overrun potential. The predictive model achieved an accuracy rate of 92.4%, which is much higher than that of the comparable model established with the full formation mechanism. This demonstrated that mechanistic models featuring a fragmentary formation mechanism well achieved satisfactory prediction accuracy and efficiency. Therefore, this predictive model provides a framework for project auditors and other relevant project management personnel to monitor project cost proactively throughout the project lifecycle. [ABSTRACT FROM AUTHOR]

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes "small group" workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both. [ABSTRACT FROM AUTHOR]

The utilization of Empty fruit bunch (EFB) in energy production has increased in Malaysia over the last two decades. The EFB can be used as a solid fuel in a boiler system for heat and power generation. However, numerous safety and technical issues lead to a lower energy production rate. A holistic probabilistic risk analysis is developed using the Bayesian Belief Network (BBN) to reduce the risk in the boiler system. The Conditional Probability Table (CPT) indicates the influence strength between the parent node and child node in BBN. Due to scarcely available information on EFB boiler, elicitation from the expert's opinion is vital. The formulation for boiler failures likelihood prediction that relies on experts' perceptions was developed using the Weighted Sum Algorithm (WSA). A case study from BioPower Plant in Pahang was applied in this project. The model illustrates the relationship between the cause and the effect of the biomass boiler efficiency in a systematic way. Two types of analyses, prediction and diagnostic analysis, were performed. The results facilitated the decision-maker to predict and identify the influential underlying factors of the boiler efficiency, respectively. The result shows that the most important boiler failure factor is combustion stability. It agrees with experts' experience that most biomass boiler failure was caused by EFB, which contains high moisture content that affects flame stability. The proposed formulation for expert opinions and perceptions conversion can be utilized for risk analysis to benefit the boiler and other infrastructure that relies on experts' experience. [ABSTRACT FROM AUTHOR]

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

Introduction: Diagnosis of high-risk maternal pregnancy is one of the most important issues during pregnancy and can be of great help to pregnant mothers. Also, early diagnosis can reduce mortality and morbidity in mothers. Material and Methods: In this study, the data of 1014 pregnant mothers were used, which includes 272 people with high-risk pregnancies, 742 people with medium-risk and low-risk pregnancies. Also, the data include six independent variables. A combination of Bayesian belief network algorithms and particle optimization was used to predict pregnancy risk. Results: For validation, the data model was divided into two sets of training and testing based on the method of 30-70. Then the proposed model was designed by training data. Then the model for training and testing data was evaluated in terms of accuracy parameters 99.18 and 98.32% accuracy were obtained, respectively. It has also performed between 0.5 and 8% better than similar work in the past. Conclusion: In this study, a new model for designing Bayesian belief network was presented and it was found that this model can be useful for predicting maternal pregnancy risk. [ABSTRACT FROM AUTHOR]

COVID‐19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy‐makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country‐level data‐set related to humanitarian crises and disasters to explore critical factors influencing COVID‐19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID‐19 risk in a probabilistic network setting. This study provides unique insights to policy‐makers regarding the identification of critical factors influencing COVID‐19 risk and their relative importance in a network setting. [ABSTRACT FROM AUTHOR]

Aiming at the problem of ignoring the impact of attack cost and intrusion intention on network security in the current network risk assessment model,in order to accurately assess the target network risk,a method of network intrusion intention analysis based on Bayesian attack graph was proposed.Based on the atomic attack probability calculated by vulnerability value,attack cost and attack benefit,the static risk assessment model was established in combination with the quantitative attack graph of Bayesian belief network,and the dynamic update model of intrusion intention was used to realize the dynamic assessment of network risk,which provided the basis for the dynamic defense measures of attack surface.Experiments show that the model is not only effective in evaluating the overall security of the network,but also feasible in predicting attack paths.

This paper proposes a probabilistic health index-based method for estimating the apparent age of power transformer. Compared with the conventional weighted-score-sum based health index, the probabilistic health index is calculated as a data fusion result of kinds of transformer condition monitoring data through a constructed Bayesian belief network. The regression result of such a probabilistic health index is then applied to estimate the apparent age of the transformer through a few steps listed in the paper. The apparent age not only embodies an overall health status a transformer but also helpful for sorting a transformer fleet based on the estimated apparent age or even make it easy to make comparisons between transformer fleets. The estimated apparent age can be taken as a reference for power utilities to prioritize transformers and pay attention to the unit who owns the maximum apparent age among a fleet, thus helps to schedule replacement plans. Case studies with different transformers verify the usability and prove the advantages of the proposed method.

An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach. [ABSTRACT FROM AUTHOR]

Gas inerting process is a great hazard for chemical tanker ships and utmost care is required considering the harmful effects to humans' health. Most of the chemical tanker ship-owners face near-miss or accident (asphyxia/fire/explosion) during gas inerting process on-board ship. The consequences of the process may pose catastrophic results such as explosion, fire or asphyxiation. Therefore, a detailed risk assessment is required to enhance the process of safety during the carriage of chemical cargo and minimize potential consequences. To address this concern, the paper aims at conducting a systematic probabilistic analysis of asphyxiation risk during gas inerting process in chemical tanker ship. In the paper, a Bayesian belief network (BBN) method is used to deal with causality and uncertainty arising from the complex interdependence between variables in the probabilistic risk assessment of asphyxiation risk. While BBN enables us to calculate the conditional probability of each intermediate node and leaf node in the graph and perform probabilistic risk assessment with sensitivity analysis, fuzzy logic deals with the translation of linguistic expressions from expert opinion into probability values. The findings of the paper will provide the utmost contribution for chemical tanker ship owners, safety researchers, maritime safety professionals, and HSEQ managers (Health, safety, environment, and quality) to prevent the risk of asphyxiation during gas inerting process in maritime transportation. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) pandemic have rapidly evolved into a situation with profound effects on lifestyle and travel worldwide, ranging from a dramatic decrease in the patronage of formal public transport modes to an unprecedented increase in the usage of informal public transport modes. It is the main goal of this study to adapt a Motorcycle Protective Isolation Shield (MPIS) on a motorcycle for the reduction of community spread of COVID-19. MPIS was designed according to the anthropometry data obtained from motorcycle riders and passengers; and was fabricated using locally available materials. Anthropometry data used for the MPIS frame were sitting height (SH) and popliteal height (PH) of both motorcycle riders and passengers while seat depth (SD) of motorcycle riders and buttock knee length (BKL) of motorcycle passengers were used to locate the position of the MPIS mounted on the motorcycle. The 50% of the anthropometry data used for the design of MPIS is SH=800.00 mm, PH=500.00 mm, SD=300.00 mm and BKL=550.00 mm. The uncertainty modelling of motorcycle accident occurrence reveals the effect of higher speeds and the adapted MPIS during riding. The probability of occurrence of fatal crash, major crash and minor crash of motorcycle accident was 36.3%, 35.3% and 28.4%, respectively. The MPIS showed satisfactory post-riding assessment in terms of riding stability; riding, steering, and sitting convenience; and riding comfort for both the riders and passengers. It will be a good protection for motorcycle riders against the risk of COVID-19 and other air-borne infections. [ABSTRACT FROM AUTHOR]

In recent decades, wildfire regimes have been changing in many parts of the world with significant differences among climatic regions. Thus, understanding the relationship between fires and driving factors in different climatic regions is important. We investigated the relative importance of wildfire drivers across climatic gradients using a Bayesian network model during 2001–2015 in the Inner Mongolia Autonomous Region (IMAR). Our results showed that regional-scale fire relative probability (FRP) on average increased in the IMAR, which was mainly attributed to increases in annual precipitation. However, changes in FRP and driving factors of wildfire occurrence varied among climatic regions. The FRP decreased at rates of −0.02/decade and −0.001/decade in the humid and semi-humid region, respectively, while increasing at an average rate of 0.05/ decade in the arid region. Temperature was recognized as the most influential driver of wildfires in the humid region, in which the relative importance of all driving factors remained relatively stable over time. In the semi-humid region, wildfires responded primarily to human activities, of which distance to railways was of greatest importance. The relative importance of distance to railways significantly increased over time, while the effects of temperature, humidity and elevation decreased considerably. Fractional vegetation cover (FVC) was the dominant factor controlling the wildfire occurrence in the arid region, in which the relative importance of precipitation significantly increased and the effect of settlements significantly decreased. Our study suggests that because of different driving factors of wildfires across climatic gradients, different fire prevention and management strategies are required. The optimal resource allocation for fire prevention should be adjusted according to the relative importance of wildfire drivers and spatiotemporal patterns of the FRP.

Southeast Asia has arguably the highest biodiversity loss due to the high deforestation rate and hunting pressure. In the region, 55 species of the family Phasianidae can be found in all available land habitats from lowland plains up to high-elevation mountainous areas. As ground-dwelling birds, these species are sensitive to habitat disturbance and hunting pressure, making them ideal to evaluate the status of remaining Southeast Asian forest habitats. The aims of this work are, therefore, to define for each Phasianidae species: (1) the extent of forest cover, suitable habitat and large forest patches currently available in the region and their decline over the years between 2000 and 2018 (six estimated generations) and (2) assess the threats using a Bayesian Belief Network approach combining data on forest loss hotspots and hunting pressure. Moreover, we defined the spatial distribution of Phasianidae diversity hotspots and relative threats in the region. The results show that over the 18-year study period, the forest cover, suitable habitat and Phasianidae diversity in Southeast Asia declined overall. The remaining forest habitats currently have low species diversity and face medium to high threat levels from habitat loss and hunting pressure. Population monitoring and higher protection levels both inside and outside protected areas are essential for the species’ long term survival. We recommend using Phasianidae as indicator species to monitor the overall habitat conservation status in Southeast Asia.

Drawing on risk methods from volcano crises, we developed a rapid COVID-19 infection model for the partial return of pupils to primary schools in England in June and July 2020, and a full return in September 2020. The model handles uncertainties in key parameters, using a stochastic re-sampling technique, allowing us to evaluate infection levels as a function of COVID-19 prevalence and projected pupil and staff headcounts. Assuming average national adult prevalence, for the first scenario (as at 1 June 2020) we found that between 178 and 924 [90% CI] schools would have at least one infected individual, out of 16 769 primary schools in total. For the second return (July), our estimate ranged between 336 (2%) and 1873 (11%) infected schools. For a full return in September 2020, our projected range was 661 (4%) to 3310 (20%) infected schools, assuming the same prevalence as for 5 June. If national prevalence fell to one-quarter of that, the projected September range would decrease to between 381 (2%) and 900 (5%) schools but would increase to between 2131 (13%) and 9743 (58%) schools if prevalence increased to 4× June level. When regional variations in prevalence and school size distribution were included in the model, a slight decrease in the projected number of infected schools was indicated, but uncertainty on estimates increased markedly. The latter model variant indicated that 82% of infected schools would be in areas where prevalence exceeded the national average and the probability of multiple infected persons in a school would be higher in such areas. Post hoc, our model projections for 1 September 2020 were seen to have been realistic and reasonable (in terms of related uncertainties) when data on schools' infections were released by official agencies following the start of the 2020/2021 academic year.

Increasing trend in global business integration and movement of material around the world has caused supply chain system susceptible to disruption involving higher risks. This paper presents a methodology for supplier selection in a global sourcing environment by considering multiple cost and risk factors. Failure modes and effects analysis technique from reliability engineering field and Bayesian belief networks are used to quantify the risk posed by each factor. The probability and the cost of each risk are then incorporated into a decision tree model to compute the total expected costs for each supply option. The supplier selection decision is made based on the total purchasing costs including both deterministic costs (such as product and transportation costs) and the risk-associated costs. The proposed approach is demonstrated using an example of a US-based Chemical distributor. This framework provides a visual tool for supply chain managers to see how cost and risks are distributed across the different alternatives. Lastly, managers can calculate expected value of perfect information to avoid a certain risk. [ABSTRACT FROM AUTHOR]

The purpose of NASA's Unmanned Aircraft Systems (UAS) Integration in the National Airspace System project is to support the FAA with research that examines the risks involved with using UAS for commercial operations. The safety risk model described in this paper aims to provide such research and is derived from a NASA Concept of Operations (ConOps). A ConOps is a document that describes a specific commercial operation for an aircraft and presents the operation's logistics, its associated environment, plausible scenarios, safety and performance considerations of the operation, and a possible plan for the execution of the operation. The specific operation defined in the studied ConOps focuses on a UAS administering a 'targeted aerial application' of agricultural chemicals to small, pre-determined areas of crops in large fields. Applying UAS to agriculture in this manner is very realistic since it is a relatively minimal risk operation compared to other potential UAS commercial operations and presents significant value to the agricultural community. [ABSTRACT FROM AUTHOR]

Fisheries social-ecological systems (SES) in the North Sea region confront multifaceted challenges stemming from environmental changes, offshore wind farm expansion, and marine protected area establishment. In this paper, we demonstrate the utility of a Bayesian Belief Network (BN) approach in comprehensively capturing and assessing the intricate spatial dynamics within the German plaice-related fisheries SES. The BN integrates ecological, economic, and socio-cultural factors to generate high-resolution maps of profitability and adaptive capacity potential (ACP) as prospective management targets. Our analysis of future scenarios, delineating changes in spatial constraints, economics, and socio-cultural aspects, identifies factors that will exert significant influence on this fisheries SES in the near future. These include the loss of fishing grounds due to the installation of offshore wind farms and marine protected areas, as well as reduced plaice landings due to climate change. The identified ACP hotspots hold the potential to guide the development of localized management strategies and sustainable planning efforts by highlighting the consequences of management decisions. Our findings emphasize the need to consider detailed spatial dynamics of fisheries SES within marine spatial planning (MSP) and illustrate how this information may assist decision-makers and practitioners in area prioritization. We, therefore, propose adopting the concept of fisheries SES within broader integrated management approaches to foster sustainable development of inherently dynamic SES in a rapidly evolving marine environment. • Fisheries socio-ecological systems (SES) are confronted with multifaceted challenges in a rapidly changing world. • Understanding their intricate spatial dynamics is vital for informed and sustainable fisheries management. • A Bayesian Belief Network assesses SES adaptive capacity and profitability revealing hotspots and offering actionable insights for fisheries management. • Dynamic fisheries SESrequire adaptive Marine Spatial Planning (MSP) and international alignment to foster adaptive capacity. [ABSTRACT FROM AUTHOR]

While the acknowledged significance of pillars such as economic sustainability, intellectual capital, resource efficiency, natural capital, social capital, and governance in contributing to a nation's long-term economic and social prosperity forms the basis of this research, our primary aim is to undertake a more nuanced exploration. Instead of reiterating their broad contributions, we seek to investigate the specific and individual roles of these pillars and the intricate interdependencies among them. To accomplish this, a Bayesian Belief Network (BBN) model was developed using a dataset by SolAbility covering sustainable competitiveness scores for 180 countries, which demonstrated a predictive accuracy of 88.9%. Sensitivity analysis uncovered the critical role played by 'governance', 'social capital', 'economic sustainability', and 'intellectual capital' in influencing overall sustainable competitiveness. Notably, 'governance' and 'intellectual capital' were found to have a far-reaching impact on multiple competitiveness pillars within the BBN model. Highly competitive countries consistently demonstrated strong performance in the 'governance', 'social capital', and 'economic sustainability' pillars, whereas low competitiveness was associated with a deficiency in 'intellectual capital'. Notably, 'governance', 'social capital', 'intellectual capital', and 'economic sustainability' emerged as the most informative factors concerning the sustainable competitiveness variable. 'Natural capital' and 'resource intensity' were relatively less critical factors. This is the first study to quantify the relative significance of individual sustainable competitiveness pillars within a probabilistic network model, thus providing valuable insights for policymakers regarding resource allocation. Researchers can also leverage these findings to delve deeper into the importance of sub-pillars associated with the critical pillars identified in this research. • BBN model predicts sustainable competitiveness with 88.9% accuracy. • Broad impact of governance and intellectual capital across competitiveness pillars. • High competitiveness is tied to strong governance and social capital. • Low competitiveness is tied to a scarcity of intellectual capital. • Governance is the most informative pillar in predicting competitiveness. [ABSTRACT FROM AUTHOR]

• A method for estimating the risk of a mid-air collision is developed. • It builds upon the Integrated Safety Assessment Model. • A Bayesian Belief Network is added to assess Data Communication system safety. • The use of Data Communication can reduce the risk of mid-air collision by 25 %. • Human communication error is more likely to cause accidents than equipment failure. This article presents a method of estimating the risk of a mid-air collision. The proposed method is an enhancement of the traditional aviation safety model - Integrated Safety Assessment Model (ISAM) - developed by the Federal Aviation Administration (FAA) and EUROCONTROL. ISAM is a mix of event-based models and fault trees that identifies causes of 35 different types of aviation accidents. While useful for conceptual understanding of accidents, the model does not handle human-technical or inter-system interactions. These drawbacks are especially evident when assessing safety impact of new communication, navigation and surveillance technologies since they rely on pilots and controllers. We propose a method of analyzing the impact of new technologies in aviation by presenting a case study of the Data Communication system – a new technology developed by the FAA used for communication between pilots and controllers. The method builds upon ISAM and leverages a Bayesian Network to estimate safety risk. The results indicate that the implementation of Data Comm can reduce the risk of collision by 25 %. In addition, if a collision has occurred, it is 10 million times more probable that the likely culprit is an error in human communication rather than a failure of communication equipment. [ABSTRACT FROM AUTHOR]

Global competitiveness refers to a nation's ability to boost its economic growth and advancement. The Global Competitiveness Index (GCI), introduced by the World Economic Forum, serves as a tool to evaluate how individual countries perform across 12 key competitiveness pillars. United Nations member states are committed to achieving the Sustainable Development Goals (SDGs) by 2030, addressing economic, social, and environmental aspects of sustainability. This research employs a data-driven Bayesian Belief Network-based method to identify crucial GCI pillars, considering their positive and negative impacts on sustainability performance. Notably, ' health ', ' infrastructure ', and ' skills ' are identified as pivotal GCI pillars with the potential to adversely affect sustainability performance. Conversely, ' innovation capability' , 'labor market ', and ' skills ' are highlighted for their positive influence on overall sustainability performance. This is the first study to explore the link between competitiveness pillars and sustainability performance in a probabilistic network framework, providing valuable insights for policymakers aiming to enhance both competitiveness and sustainability. • A BBN model is developed to explore dependencies among GCI pillars and SDG index. • Two datasets are used to calibrate and validate the model. • A comprehensive sensitivity analysis is performed to prioritize GCI pillars based on their impact on sustainability. • The diagnostic assessment of GCI pillars in predicting the SDG index is performed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic changed daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution systems affected water quality. A range of factors can influence individual decisions to social distance, including demographics, risk perceptions, and prior experience with infectious disease. This research develops an agent-based modeling framework to simulate decisions to social distance, the effect of social distancing on water demands, and effects on the performance of water infrastructure and the quality of delivered drinking water. This framework couples a hydraulic model, a COVID-19 transmission model, and Bayesian belief network (BBN) driven decision-making models within an agent-based modeling framework. The model is applied for a virtual city, Micropolis, to explore the effects of social distancing decisions on water age. Results demonstrate an increase in average water age and changes to the expected flow directions in pipes under scenarios of increasing social distancing. Nodes near industrial areas experience higher degradation of water quality. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics. • Agent-based model simulates disease transmission and COVID-19 social distancing. • Agent-based model is coupled with hydraulic model to assess demand and water age. • Social distancing led to spatial changes in water demand and increased water age. • Residential nodes near industrial areas were affected by high water age. [ABSTRACT FROM AUTHOR]