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.

Theory-Guided Bayesian Analysis for Modeling Impact of COVID-19 on Gross Domestic Product

GDP or Gross Domestic Product is a key indicator of economic status, which provides an omni-comprehensive measure of the wealth of a country or a state. With the sudden proliferation of novel coronavirus disease (COVID-19), there has been increasing interest in forecasting GDP, since this may be severely impacted by the various pandemic control measures imposed in recent days. An accurate forecast of GDP can extensively help in putting forth right administrative measures while ensuring minimum disruption in economy. Though the recent researches focus on various machine learning-based data-driven models for this purpose, these primarily analyze the change in observed GDP data without explicitly modeling the pandemic impact. We address this issue by proposing a novel approach that incorporates epidemiological insights into Bayesian network-based predictive analytics to account for the influence of COVID-19 development on the GDP. Rigorous experimentation on state-level and country-level datasets of India demonstrates that a judicious combination of theoretical and data-driven models can substantially improve GDP forecast performance. Our model produces an average prediction error of 0.002% and outperforms several state-of-the-art techniques with a large margin. © 2022 IEEE.

COVID-19 risk factors specification using Decision Tree based on the degree of redundancy between features

Based on the latest diseases which spread in the whole world and need to be predicted and classified. In addition, when testing and examining the samples will be safer with far data collecting such as COVID-19 cases. Therefore;this research provides a safe and accurate data mining prediction system to make a decision with high performance to prevent this spread. Such a study prevents or at least reduces the effect of contacting suspicious patients with others by providing a discovery system to detect this disease in these samples. Also, this study will reduce the effects of COVID-19 on marketing, teaching, and other different business, which lead to holding this disease separated at home with high knowledge of some symptoms that will be studied to specify the most affected features on this classification. However, this study could provide some information about viruses moving and keeping away at home with an early prediction. In this study, three techniques are applied for 1486 patients after data preprocessing and preparing for the performance evaluation. Risk factors are determined using a features selector and study of the effect of these features before and after minimization on the whole proposed model. Differences and reasons are shown in this paper due to different results which occurred while omitting unnecessary data. All the proposed models showed an enhancement in their performances after selecting the most affected features. But, DT showed the best prediction accuracy with about 96% compared to other models. On the other hand, other parameters are explained and showen some more advanced in the DT model than in other models. © 2022 IEEE.

Intelligent Technology for Predicting the Risk of Patient's Death from Coronavirus Based on PRINCIPLE-Methodology for Selecting Indicators Collected from Medical Devices

In Ukraine, COVID-19 has contributed over 104,106 deaths. Multiple risk factors for COVID-19 almost have been identified. The new PRINCIPLE methodology for selecting indicators of patient screening using medical equipment is developed. The name of this methodology is an acronym of the criteria for selecting indicators: 'Provability', 'Reproducibility', 'Informativeness', 'Numerical', 'Clinical', 'Importance', 'Prevalence', 'Lungs', 'Electrocardiography'. Tools based on the Bayesian network to predict the high risk of patient mortality based on these indicators are developed. An example of application of the proposed methodology, construction of the model, and formation of conclusions by them are given for anonymized data consisting of 22 features from adults 280 alive and 140 dead patients, diagnosed with COVID-19 at the hospital in Vinnytsia. The work offers an improved method for processing and analyzing the biomedical indicators and medical diagnostic data for the clinical decision-making tool for COVID-19 inpatients construction. © 2022 IEEE.

Assessing Resilience of Urban Transportation Systems Under Serious Epidemic Situation

Aiming at the role of urban transportation systems in the prevention and control of the new crown pneumonia epidemic and emergency support. Based on epidemic prevention and control, this paper introduced the concept of resilience. The change process of system performance was divided into the prevention stage, maintenance stage, and recovery stage. Analyzed the factors affecting urban transportation systems resilience at various stages and the causal relationship between the factors. Assessment indicators of the transportation system resilience was established. Bayesian network (BN) was used to build resilience assessment model of urban transportation systems. And BN was used to evaluate and reason the model. Taking Xi'an as an example, the paper assessed the resilience of Xi'an transportation system. GeNIe was used for causal inference and sensitivity analysis of the network. Identify factors with high sensitivity and propose improvement measures. The results show that the model can quantify the resilience of urban transportation systems during the COVID-19 Pandemic, evaluate the current situation of the systems, and analyze the effects of factors on the resilience, to provide decision support for improving the resilience of urban transportation systems and dealing with epidemic risk. © 2021 SPIE

Hybrid Modeling for Predicting Inpatient Treatment Outcome: COVID-19 Case

This study presents two methods to support the treatment process of inpatients with COVID-19. The first method is designed to predict treatment outcomes;this method is based on machine learning models and probabilistic graph models of patient clustering. The method demonstrates high quality in terms of predictive models, and the structure of the graph model is supported by knowledge from practical medicine and other studies. The method is used as a basis for finding the optimal intervention plan for severe patients. This plan is a set of interventions for patients that are optimal in terms of minimizing the probability of mortality. We tested the method for critically ill patients (item 4.5) and for 30% of all patients with lethal outcomes the methods found an intervention plan that leads to recovery as a treatment outcome as predicted. Both methods show high quality, and after validation by physicians, this method can be used as part of a decision support system for medical professionals working with COVID-19 patients. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Bayesian Network-Based Model to Understand the Role of Soft Requirements in Technology Acceptance: the Case of the NHS COVID-19 Test and Trace App in England andWales

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of software products, where soft requirements need to be taken into account. Therefore, we address this need by using a novel Bayesian network approach that allows the prediction of end users' decisions and ranks soft requirements' importance when making these decisions. The approach offers insights that help requirements engineers better understand which soft requirements are essential for particular software to be accepted by its target users. We have implemented a Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied to the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). Our findings show that soft requirements such as Responsibility and Trust (e.g. Trust in the supplier/brand) are relevant for the COVID-19 app acceptance. However, the importance of soft requirements is also contextual and time-dependent. For example, Fear of infection was an essential soft requirement, but its relevance decreased over time. The results are reported as part of a two stage-validation of the model. © 2022 ACM.

Application of Bayesian Network Reasoning Algorithm in Emotion Classification

In the early stage of covid-19 disease transmission, it is easy to lead to public panic and dissatisfaction without timely information feedback. In order to solve this problem, this paper constructs an emotion classification and prediction algorithm based on Bayesian network reasoning by analyzing the variable elimination algorithm, connection tree reasoning algorithm and Gibbs sampling algorithm in Bayesian network reasoning algorithm. The algorithm can quickly identify the emotions of Internet users from the communication with low computational resources, and provide reference for the relevant departments to formulate the correct public opinion guidance strategy. © 2021 IEEE.

A Fuzzy Bayesian Network Based on Fault Tree for Vaccine Safety Risks Analysis

Against the Covid-19 background, vaccine safety has aroused the wild attention of all social areas. However, the factors that cause vaccine safety risks are complicated and meanwhile, data is difficult to obtain, making it a challenge for analyzing vaccine safety risks quantitatively. This paper concretises the issue of vaccine system safety by creatively proposing an analytical framework for the problem of uncertainty. First, the paper focuses on the whole process of vaccine safety, analyses risk factors affecting vaccine safety in development, approval, production, transportation, and supervision of vaccines in order to build a vaccine risk assessment system. The proposed framework is then used to construct a Bayesian network early warning system for vaccine risk. To address the difficulty of obtaining data, the probability of safety risks occurring throughout the process is calculated by combining expert knowledge and fuzzy set theory to obtain uncertainty data. In response to structural complexity, a comprehensive framework is constructed using fault trees and Bayesian networks to capture the correlation between risk factors. This analytical framework can provide guidance to governments and vaccine-related companies in their decision-making to prevent vaccine safety issues. Finally, sensitivity analysis revealed a high probability of vaccine risk in the transport process. © 2021 IEEE.

Using Bayesian Belief Network to Solve Abnormal Symptoms after Vaccination under Modern Information Technology

Rare side effects are weakening confidence in the vaccine. The question is how we interpret the data. Within 15 months after the discovery of the new coronavirus, a variety of effective and safe vaccines against the new coronavirus were available. After receiving the new coronavirus vaccine, some people developed facial paralysis, thigh pain, and even cerebral venous thrombosis. Although these side effects are very rare, and there is a lack of clarity whether there is a causal relationship with the vaccine or not, such news may undermine the confidence of the global vaccine. In order to maintain the confidence of the public, adverse events after vaccination are called ordinary events, and deaths occurring within a few days after vaccination are also interpreted as being caused by their latent diseases. From the following research, the issue of causality divides the vaccinated population into healthy groups and long-term patient groups, and use Bayesian belief network to analyze whether there are symptoms or abnormal events after vaccination as well as the probability distribution of rare illness, death, etc., in order to understand the relationship among each other. Therefore, suspending the administration of COVID vaccine is not a zero-risk option. The reality is that nothing is without risk. Measures to mitigate a risk must be balanced with competitive hazards. Risk seems to be an and vague concept. Risk can be reduced, but it can never be eliminated. The advantage of the Bayesian model is that it is easy to bring the data of various variables into the graph and calculate the posterior data from the known data to strengthen the persuasiveness of vaccination. By using Bayesian Network with PGM Module of Pytorch, the death probability of these two groups can be calculated under abnormal symptoms or without them. The simulation result of death after inoculation is lower than that of normal state without Covid-19 pandemic. © 2021 ACM.

Discrete Latent Variables Discovery and Structure Learning in Mixed Bayesian Networks

Latent variables pose a challenge for accurate modelling, experimental design, and inference, since they may cause non-adjustable bias in the estimation of effects. While most of the research regarding latent variables revolves around accounting for their presence and learning how they interact with other variables in the experiment, their bare existence is assumed to be deduced based on domain expertise. In this work we focus on the discovery of such latent variables, utilizing statistical hypothesis testing methods and Bayesian Networks learning. Specifically, we present a novel method for detecting discrete latent factors which affect continuous observed outcomes, in mixed discrete/continuous observed data, and device a structure learning algorithm that adds the detected latent factors to a fully observed Bayesian Network. Finally, we demonstrate the utility of our method with a set of experiments, in both controlled and real-life settings, one of which is a prediction for the outcome of COVID-19 test results. © 2021 IEEE.

ALFABETO: Supporting COVID-19 hospital admissions with Bayesian Networks

The ongoing pandemics of coronavirus disease has accelerated the implementation of machine learning methods (ML) to support clinical decisions. Within this context, we present the ALFABETO project, whose aim is to aid clinicians during COVID-19 patients hospital admission through the application of ML approaches exploiting clinical and chest x-ray features. Yet, non linear ML classifiers are often perceived as not easily interpretable by users, thus hampering trust in ML predictions. Moreover, these ML models, such as Neural Networks or Random Forest, are not able to include pre-exisisting knowledge about a specific domain and are not designed to find causal relationships between variables. For these reasons, we wanted to investigate if Bayesian Networks were able to properly describe the hospital admission decision process. Bayesian Networks are probabilistic graphical models representing a set of variables and their conditional dependencies. The network structure was derived both from existing medical knowledge and from patients data collected during the first wave of the pandemic. While being explainable, we show that the Bayesian network has similar performance when compared to a less explainable ML model and that was able to generalize well across COVID-19 waves. © 2021 Copyright for this paper by its authors.