Monitoring and Early Warning of SMEs' Shutdown Risk under the Impact of Global Pandemic Shock

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input-output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

A Three-Stage Hybrid SEM-BN-ANN Approach for Analyzing Airport Service Quality

The novel coronavirus (COVID-19) outbreak has impacted the aviation industry worldwide. Several restrictions and regulations have been implemented to prevent the virus's spread and maintain airport operations. To recover the trustworthiness of air travelers in the new normality, improving airport service quality (ASQ) is necessary, ultimately increasing passenger satisfaction in airports. This research focuses on the relationship between passenger satisfaction and the ASQ dimensions of airports in Thailand. A three-stage analysis model was conducted by integrating structural equation modeling, Bayesian networks, and artificial neural networks to identify critical ASQ dimensions that highly impact overall satisfaction. The findings reveal that airport facilities, wayfinding, and security are three dominant dimensions influencing overall passenger satisfaction. This insight could help airport managers and operators recover passenger satisfaction, increase trustworthiness, and maintain the efficiency of the airports in not only this severe crisis but also in the new normality.

Chinese travellers' mobility decision-making processes during public health crisis situations: a Bayesian network model

Travellers' mobility decisions are fraught with uncertainty and instability during public health crises. However, existing studies have not revealed the internal mechanism of travellers' mobility changes in a public health crisis. This paper established and trained a Bayesian network model from multiple data to analyse Chinese travellers' mobility decision-making processes under COVID-19 and simulated the changes in mobility decisions in different scenarios. The results show that travellers reformulate mobility decisions in response to various information and negotiate between social customs and personal needs. Mobility can be modified through risk communication and habits adaptation. Bayesian network models provide a methodological contribution to causal exploration and scenario prediction.

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 Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.

Antithrombotic strategies in COVID-19 patients: a systematic review and Bayesian network meta-analysis

Introduction: COVID-19 is a public health emergency of international concern. Clinicians are likely to adopt various antithrombotic strategies to prevent embolic events, but the optimal antithrombotic strategy remains uncertain. We performed a Bayesian network meta-analysis to evaluate various antithrombotic strategies comprehensively. Method(s): We systematically searched PubMed, Cochrane Library, Web of Science, EMBASE and Clinical trials. gov to screen trials comparing different antithrombotic strategies. The primary outcome is 28-day mortality, and the secondary outcomes include major thrombotic event, major bleeding and in-hospital mortality, etc. We assessed the risk of bias using the Cochrane Collaboration's tool and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We successively performed traditional pairwise and Bayesian network meta-analysis using R v4.2.1 software. Result(s): Twenty-six eligible randomized controlled trials were included, giving a total of 35 paired comparisons with 32,041 patients randomized to 7 antithrombotic strategies. In comparison to standard of care (SoC) strategy, therapeutic anticoagulation (TA) (RR 0.36, 95% CrI 0.13-0.86) and prophylactic anticoagulation (PA) (RR 0.35, 95% CrI 0.12-0.85) strategy significantly reduced the mortality of COVID-19 patients (Fig. 1). The antiplatelet (AP) strategy was associated with high risk of major bleeding when compared with SoC strategy (RR 2.5, 95% CrI 1.1-8.9), and the TA (RR 0.43, 95% CrI 0.17-0.98), PA (RR 0.27, 95% CrI 0.10-0.63) and PA with Fibrinolytic agents (FA) strategy (RR 0.12, 95% CrI 0.01-0.81) was associated with low risk of major thrombotic event. Conclusion(s): This network meta-analysis indicates that the TA and PA strategies probably reduce mortality and confer other important benefits in COVID-19 patients. These findings provide guidance on how to choose optimal antithrombotic strategies for COVID-19 patients.

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment.

Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. In view of this challenge, a new model is proposed in this paper for assignment of monetary losses terms to the consequences nodes, thereby enabling experts to understand better the financial risks of any consequence. The proposed model is named Cloud Enterprise Dynamic Risk Assessment (CEDRA) model that uses CVSS, threat intelligence feeds and information about exploitation availability in the wild using dynamic Bayesian networks to predict vulnerability exploitations and financial losses. A case study of a scenario based on the Capital One breach attack was conducted to demonstrate experimentally the applicability of the model proposed in this paper. The methods presented in this study has improved vulnerability and financial losses prediction.

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints.

BACKGROUND: Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use. METHOD: In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints. RESULTS: After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07-0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impacts. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints. CONCLUSION: Alcohol, nicotine, and cannabis use had unique associations to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links.

A Study on the Bayesian Network Approach with K-Anonymity in Contact Tracing Applications for COVID-19

Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-Anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-Anonymity algorithm is utilized to prevent malicious users from accessing patients' personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the K-Anonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with k-Anonymity is 97.058%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-Anonymity has an accuracy score of 97.1429%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions. © 2022 IEEE.

Trust management in the Internet of medical things

In recent years, as a new technology based on the extensive connection of objects in heterogeneous networks, the Internet of Things (IoT) paradigm has been introduced and has attracted people's attention. We propose a robust management model of the IoMT network based on social media concepts and priority standards. The rapid development of Internet of things (IoT) technology in the field of health care has brought a variety of security threats and risks. With the increasing use of sensor objects in the medical field, providing comprehensive protection has become a major challenge, which has led to the emergence of the Internet of things. The definition of Internet of Medical Things is that medical devices are connected with each other and IT infrastructure of the organization through Internet. It is a serious problem and said the problem is increasing day by day due to more security, privacy, and confidentiality issues. In addition, it uses the capabilities of available equipment to improve the health of patients. IoMT also offers many opportunities to innovate health care and provide multiple options for precision surgery. OSN can benefit many sensitive applications, such as e-health and medical services. The emerging field of the Internet of medical things (IoMT) is promoting trust between various IoMT devices to ensure accurate and reliable communication, which is very important for major diseases such as COVID-19. To cope with this situation, a trust management mechanism called a fuzzy trust management mechanism is proposed to prevent Sybil attacks on the Internet of medical things (FTM-IoMT). The IoMT security can be enhanced through multifactor verification based on fuzzy logic and fuzzy filtering processing. Compared with more advanced methods, the proposed scheme shows better results. The main goal is to study the dynamic environment in the medical field and to realize adaptive access control. The security of the IoMT system is gradually improving. It reduces communication delays, proactively manages security risks, and ensure data protection for patients and doctors in health care. © 2023 Elsevier Inc. All rights reserved.

Risk assessment of infectious disease epidemic based on fuzzy Bayesian network.

The prevention and control of infectious disease epidemic (IDE) is an important task for every country and region. Risk assessment is significant for the prevention and control of IDE. Fuzzy Bayesian networks (FBN) can capture complex causality and uncertainty. The study developed a novel FBN model, integrating grounded theory, interpretive structural model, and expert weight determination algorithm for the risk assessment of IDE. The algorithm is proposed by the authors for expert weighting in fuzzy environment. The proposed FBN model comprehensively takes into account the risk factors and the interaction among them, and quantifies the uncertainty of IDE risk assessment, so as to make the assessment results more reliable. Taking the epidemic situation of COVID-19 in Wuhan as a case, the application of the proposed model is illustrated. And sensitivity analysis is performed to identify the important risk factors of IDE. Moreover, the effectiveness of the model is checked by the three-criterion-based quantitative validation method including variation connection, consistent effect, and cumulative limitation. Results show that the probability of the outbreak of COVID-19 in Wuhan is as high as 82.26%, which is well-matched with the actual situation. "Information transfer mechanism," "coordination and cooperation among various personnel," "population flow," and "ability of quarantine" are key risk factors. The constructed model meets the above three criteria. The application potential and effectiveness of the developed FBN model are demonstrated. The study provides decision support for preventing and controlling IDE.

Network models for cyber attacks evaluation

The significant recent growth in digitization has been accompanied by a rapid increase in cyber attacks affecting all sectors. Thus, it is fundamental to make a correct assessment of the risk to suffer a cyber attack and of the resulting damage. Quantitative loss data are rarely available, while it is possible to obtain a qualitative evaluation on an ordinal scale of the gravity of an attack from experts of the sector. In this paper, we discuss how network models can be useful instruments for the evaluation of the risk associated to a cyber attack. In particular, we consider Bayesian Networks, Random Forests and Social Networks to study different aspects of the examined problem. Along with the description of the methodology, we examine a real set of data regarding serious cyber attacks occurred worldwide before and during the pandemic due to Covid-19. In the analysis, we also investigate how the Covid-19 period had an impact on the cyber risk landscape in terms of frequency and gravity of the observed attacks. © 2023 Elsevier Ltd

Ripple effect mitigation capabilities of a hub and spoke distribution network: an empirical analysis of pharmaceutical supply chains in India

In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.

Estimating national-level measles case-fatality ratios in low-income and middle-income countries: an updated systematic review and modelling study

Background: To understand the current measles mortality burden, and to mitigate the future burden, it is crucial to have robust estimates of measles case fatalities. Estimates of measles case-fatality ratios (CFRs) that are specific to age, location, and time are essential to capture variations in underlying population-level factors, such as vaccination coverage and measles incidence, which contribute to increases or decreases in CFRs. In this study, we updated estimates of measles CFRs by expanding upon previous systematic reviews and implementing a meta-regression model. Our objective was to use all information available to estimate measles CFRs in low-income and middle-income countries (LMICs) by country, age, and year. Method(s): For this systematic review and meta-regression modelling study, we searched PubMed on Dec 31, 2020 for all available primary data published from Jan 1, 1980 to Dec 31, 2020, on measles cases and fatalities occurring up to Dec 31, 2019 in LMICs. We included studies that previous systematic reviews had included or which contained primary data on measles cases and deaths from hospital-based, community-based, or surveillance-based reports, including outbreak investigations. We excluded studies that were not in humans, or reported only data that were only non-primary, or on restricted populations (eg, people living with HIV), or on long-term measles mortality (eg, death from subacute sclerosing panencephalitis), and studies that did not include country-level data or relevant information on measles cases and deaths, or were for a high-income country. We extracted summary data on measles cases and measles deaths from studies that fitted our inclusion and exclusion criteria. Using these data and a suite of covariates related to measles CFRs, we implemented a Bayesian meta-regression model to produce estimates of measles CFRs from 1990 to 2019 by location and age group. This study was not registered with PROSPERO or otherwise. Finding(s): We identified 2705 records, of which 208 sources contained information on both measles cases and measles deaths in LMICS and were included in the review. Between 1990 and 2019, CFRs substantially decreased in both community-based and hospital-based settings, with consistent patterns across age groups. For people aged 0-34 years, we estimated a mean CFR for 2019 of 1.32% (95% uncertainty interval [UI] 1.28-1.36) among community-based settings and 5.35% (5.08-5.64) among hospital-based settings. We estimated the 2019 CFR in community-based settings to be 3.03% (UI 2.89-3.16) for those younger than 1 year, 1.63% (1.58-1.68) for age 1-4 years, 0.84% (0.80-0.87) for age 5-9 years, and 0.67% (0.64-0.70) for age 10-14 years. Interpretation(s): Although CFRs have declined between 1990 and 2019, there are still large heterogeneities across locations and ages. One limitation of this systematic review is that we were unable to assess measles CFR among particular populations, such as refugees and internally displaced people. Our updated methodological framework and estimates could be used to evaluate the effect of measles control and vaccination programmes on reducing the preventable measles mortality burden. Funding(s): Bill & Melinda Gates Foundation;Gavi, the Vaccine Alliance;and the US National Institutes of Health.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation.

Background: The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. Methods: Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples (n = 1,005 Austria; n = 1,006 UK), sociodemographic features and a multi-domain battery of health, wellbeing and quality of life (QOL) measures were completed. Predictive accuracy was examined using the area under the curve (AUC) within-sample (country) and out-of-sample. Results: The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment. Conclusions: Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts.

BACKGROUND: COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. METHODS: In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. RESULTS: We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. CONCLUSIONS: Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

Uncovering the behavioral determinants behind private car purchase intention during the new normal of COVID-19: An empirical investigation in China.

Based on the Protection Motivation Theory (PMT), the Psychological Reactance Theory (PRT), and the Theory of Planned Behavior (TPB), we revealed the psychological impact factors of individuals' private car purchase intentions during the new normal of COVID-19. Structural equation modeling (SEM) and Bayesian network (BN) were used to analyzed the car purchase decision-making mechanism. A questionnaire survey was conducted to collect empirical data from April 20th to May 26th of 2020 in China. We investigated 645 participants and analyzed the data. The SEM results showed that conditional value, pro-car-purchasing attitude, and perceived behavioral control, health value, and cost factors have significant direct effects on car purchase intention. According to BN's prediction of purchase intention, the probability of high purchase intention grew by 47.6%, 97.3% and 163.0%, respectively, with perceived behavioral control, pro-car-purchasing attitude, and conditional value shifting from "low" to "medium" and "high". This study provided a new perspective for researchers to explore the purchase intention of cars during the epidemic. Meanwhile, we could provide a reference for the government and enterprises to develop measures related to the automobile market."

The global dynamic transmissibility of COVID-19 and its influencing factors: an analysis of control measures from 176 countries.

OBJECTIVE: To summarise the dynamic characteristics of COVID-19 transmissibility; To analyse and quantify the effect of control measures on controlling the transmissibility of COVID-19; To predict and compare the effectiveness of different control measures. METHODS: We used the basic reproduction number ([Formula: see text]) to measure the transmissibility of COVID-19, the transmissibility of COVID-19 and control measures of 176 countries and regions from January 1, 2020 to May 14, 2022 were included in the study. The dynamic characteristics of COVID-19 transmissibility were summarised through descriptive research and a Dynamic Bayesian Network (DBN) model was constructed to quantify the effect of control measures on controlling the transmissibility of COVID-19. RESULTS: The results show that the spatial transmissibility of COVID-19 is high in Asia, Europe and Africa, the temporal transmissibility of COVID-19 increases with the epidemic of Beta and Omicron strains. Dynamic Bayesian Network (DBN) model shows that the transmissibility of COVID-19 is negatively correlated with control measures. Restricting population mobility has the strongest effect, nucleic acid testing (NAT) has a strong effect, and vaccination has the weakest effect. CONCLUSION: Strict control measures are essential for controlling the COVID-19 outbreak; Restricting population mobility and nucleic acid testing (NAT) have significant impacts on controlling the COVID-19 transmissibility, while vaccination has no significant impact. In light of these findings, future control measures may include the widespread use of new NAT technology and the promotion of booster immunization.

Bayesian Network Model of China's Financial Risk Under COVID-19 Based on Grey Clustering

The panic caused by COVID-19 and the stagnation of business activities induced the continuous breeding of China's financial risks. This paper considers the COVID-19 and economic indexes as nodes to establish the Bayesian topology of financial risk. The liquidity, sovereign, and stock market risks are mainly considered to evaluate the financial risk. Based on the risk characteristics, the central interval trapezoidal possibility functions are designed, then the grey clustering model is used to classify the financial risk into four different levels. The possibility distribution of financial risk levels under different COVID-19 index levels is inferenced through the Bayesian network. Finally, each node's monthly time series data from October 2019 to May 2021 is used to learn by NETICA software, and the conditional probability of each node and the possibility of financial risk are deduced. It is concluded that liquidity risk and sovereign risk are more sensitive to COVID-19, while the stock market risk is not very sensitive to it.