ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

EVALUATING THE ETHICAL AND SOCIETAL IMPACTS OF MODELLING PANDEMIC CRISES: EXPERIENCES FROM A WORKSHOP

This paper describes a hybrid (virtual and online) workshop held as part of the EU STAMINA project that aimed to engage project partners to explore ethics and simulation modelling in the context of pandemic preparedness and response. The purpose of the workshop was to consider how the model's design and use in specific pandemic decision-making contexts could have broader implications for issues like transparency, explainability, representativeness, bias, trust, equality, and social injustices. Its outputs will be used as evidence to produce a series of measures that could help mitigate ethical harms and support the greater possible benefit from the use of the models. These include recommendations for policy, data-gathering, training, potential protocols to support end-user engagement, as well as guidelines for designing and using simulation models for pandemic decision-making. This paper presents the methodological approaches taken when designing the workshop, practical concerns raised, initial insights gained, and considers future steps. © SW 2023.All rights reserved

Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.

COVID-19 and the Indoor Environment - observations from a joint National Academies of Engineering seminar series

The SARS-CoV-2 virus and its variants and COVID-19 disease have affected every aspect of society. The US National Academy of Sciences has been providing scientific insights and advice to aid policymakers and researchers in their quest to respond to the pandemic. Since 2020, it has produced numerous reports and workshop proceedings intended to integrate science into national preparedness and response decision-making, to explore lessons learned and best practices from previous preparedness and response efforts, and to consider strategies for addressing misinformation (NASEM, 2021). Among these was a 2021 symposium series that analyzed engineering's role in catalyzing COVID-19 response, recovery, and resilience, examining topics including the mitigation of exposure in public transit systems, engineering solutions to managing pathogens indoors, and the factors influence the transmission of infectious diseases in cities. Speaker presentations addressing these indoor environment topics are summarized here. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Visual analysis of the trend of mental health changes among Chinese college students in the context of the normalisation of the new crown epidemic

Public health events are sudden, public in nature and have serious social hazards. The COVID-19 outbreak coincided with the Lunar New Year, which had a direct or indirect impact on all areas of society. Previous studies related to emergencies have found that a considerable number of college students lacked experience in dealing with emergencies, were not emotionally stable enough, lacked analysis and decision-making ability, were easily suggestible and acted more impulsively. Therefore, in this paper, based on the existing actual information, combined with the awareness and understanding of college students' mental health, and based on the existing research results, the Hopfield-mental health model is used as a theoretical basis to study the trend of changes in college students' mental health. The results of the study show that 83.21% of the people are more concerned about the situation of this new crown pneumonia epidemic and they think that the new crown epidemic has seriously affected their living habits;65.45% thought that this new crown pneumonia epidemic did not have any major impact on their school life. The five sources of psychological stress, including academic, employment, economic, interpersonal relationship and love, were calculated and analysed in the model, which showed that employment stress, academic stress and economic stress were the largest sources of psychological stress among college students in this new pneumonia epidemic, accounting for 89%, 81% and 93%, respectively. They were followed by interpersonal and romantic stress, with 31% and 52%, respectively. © 2022 Liping Zhang.

New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.

On Novel Hellinger Divergence Measure of Neutrosophic Hypersoft Sets in Symptomatic Detection of COVID-19

Numerous decision science processes involves the divergence measure is the most suitable information measure for dealing with the vagueness and impreciseness of the factors affecting the decision-making models. In this manuscript, a new kind of Hellinger information measure for a single-valued neutrosophic hypersoft set along with some important results have been presented and studied in detail. Also, we have presented the implementation of the proposed information measure to deal with the symptomatic detection of COVID-19 with a numerical illustration. In view of the existing methods related to divergence measures, some comparative results and remarks along with some important advantages have also been presented © 2023, Neutrosophic Sets and Systems.All Rights Reserved.

Forecasting of COVID-19 Dynamics by Agent-Based Model

The novel coronavirus pandemic has continued to spread worldwide for more than two years. The development of automated solutions to support decision-making in pandemic control is still an ongoing challenge. This study aims to develop an agent-based model of the COVID-19 epidemic process to predict its dynamics in a specific area. The model shows sufficient accuracy for decision-making by public health authorities. At the same time, the advantage of the model is that it allows taking into account the stochastic nature of the epidemic process and the heterogeneity of the studied population. At the same time, the adequacy of the model can be improved with a more detailed description of the population and external factors that can affect the dynamics of the epidemic process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Media and Healthcare Technology in 2022: Macro analytics, text and data mining, social media platforms and COVID-19

Social media has changed the way individuals and institutions approach healthcare and health information and offers opportunities to understand health-related interactions at all levels, from the micro to the macro. The Social Media and Healthcare Technology mini-track presents research papers that address a diverse array of social media and associated technology within healthcare and healthcare research;including macro analytics, text and data mining and the role of social media platforms and influencers in health care and health-related decision making. © 2022 IEEE Computer Society. All rights reserved.

Towards Zoomocracy - an Explorative Study on Virtual Democratic Decision Making in Swedish City Council Meetings in the Wake of the Covid-19 Pandemic

Governmental decision making is essential to maintain democracy. The decision making formats and processes are institutionalized and follow strict formats for participation, debate and decisions. The constraints and lock-downs due to the covid-19 pandemic led to an extensive increase in the use of digital meeting tools to maintain democratic decision making through virtual meetings. Our main approach in this paper is to inductively explore the changes that occur when democratic meetings take place on-line through a quantitative text analysis and interviews. We delimit our focus to speech duration in recorded meetings. We find that the virtual meeting format changed meeting characteristics compared to on-site meetings. There were some changes in speech duration among councilors which has to be further investigated in a larger sample. The main contribution of this paper is the method to measure actual speech duration and compare how virtual meetings may influence the organization of democratic meetings. © 2022 IEEE Computer Society. All rights reserved.

A Review of the Application of Artificial Intelligence in Medicine: From Data to Personalised Models

Artificial intelligence leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers decision making of clinicians. Starting from data (medical images, biomarkers, patients' data) and using powerful tools such as convolutional neural networks, classification, and regression models etc., it aims at creating personalized models, adapted to each patient, which can be applied in real clinical practice as a decision support system to doctors. This chapter discusses the use of AI in medicine, with an emphasis on the classification of patients with carotid artery disease, evaluation of patient conditions with familiar cardiomyopathy, and COVID-19 models (personalized and epidemiological). The chapter also discusses model integration into a cloud-based platform to deal with model testing without any special software needs. Although AI has great potential in the medical field, the sociological and ethical complexity of these applications necessitates additional analysis, evidence of their medical efficacy, economic worth, and the creation of multidisciplinary methods for their wider deployment in clinical practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The stratified multi criteria decision making method: An extended version

It often occurs that after a multi-criteria decision is made, the decision maker becomes unsure as to whether they have made the best decision. This doubt arises because the criteria being considered do not carry the same weightings. This instability is relevant to the consideration of possible future events, such as a possible recession following the COVID-19 outbreak, which may affect the criteria weightings. The stratified multi-criteria decision-making method (SMCDM) has been proposed to address this issue. This method suggests the consideration of a number of states in the decision-making process. In each state, the weightings of the criteria are different depending on which event or which combination of events are being considered. The states are associated with transition probabilities that are used to compute the optimal weightings of the criteria. This paper suggests approaches to compute the transition probabilities. Moreover, the consideration of several events in SMCDM results in a great number of states and this would be a time consuming and error prone process. Hence, the incremental enlargement characteristic of the concept of stratification (CST) is added to SMCDM in order to reduce the large numbers of states to a manageable quantity. IEEE

Decision-Making in Mask Disposal Techniques Using Soft Set Theory

In 2003, Maji, Biswas, and Roy developed a method for applying soft set theory to a decision-making problem using Pawlak's rough set approach. Further, research proved that Maji's soft set reductions were inaccurate in 2005, leading to the development of a new method by Chen et al. This article applies soft theory to waste management and disposal decision-making problems. The excessive masks discarded during the COVID-19 era, in particular, must be managed effectively, and the current paper provides a method for better decision-making of the same. The algorithms used are first to compute the reductions and then the reduct soft set is used to choose the ideal objects for decision problems, and then the choice value is calculated. Predefined parameters are sometimes not enough to make precise decisions to solve general or real-time issues. Therefore, additional parameters are added into the existing set, either as a new parameter or generated by the handling of existing ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Wastewater Surveillance Program Management and Execution: Lessons Learned from the COVID-19 Pandemic

Wastewater surveillance is a disease-tracking tool that supports early response to infectious diseases, such as public health decision-making and vaccination efforts. Public health agencies have partnered with local officials, wastewater utilities, research institutions, engineers, and physicians to implement practical wastewater surveillance programs. Program development involves careful planning of sampling sites via geographic information system (GIS) analysis, safe and efficient sampling support, analytical methodology development, data analysis and management, and collaboration between wastewater utilities and public health officials. We are focused on building these partnerships to facilitate wastewater testing programs around the globe for the COVID-19 pandemic and preparing for future disease outbreaks. The primary goal of this presentation is to share lessons learned from real-world wastewater surveillance programs to support widespread adoption. We aim to present case studies developed at facility, neighborhood, city, and statewide scales to discuss benefits and challenges of the approach. Copyright © 2022 Water Environment Federation.

Acting Egoistically in a Crisis: How Emotions Shape Data Donations

The spread of COVID-19 has affected all of us, be it financially, socially, or even physically. It has caused uncertainty and anxiety, which has put people into a "hot" mental state. Referred to as an empathy gap, people are assumed to make emotion-driven decisions in "hot" states compared to "cold" states, which contrasts with the normative assumption of rational decision-making in privacy research. Based on an experimental survey study among 445 participants, we investigate whether people's mental state interacts with individuals' information disclosure decision-making. We measure our research model in the context of actual health data donation, which constitutes a critical surveillance factor in the COVID-19 crisis. Thereby, we contribute to research by (1) investigating data donation behavior amid a crisis and (2) helping to explain further nuances of privacy decision-making and the importance of trust as a context-dependent driver of data donation. © 2022 IEEE Computer Society. All rights reserved.

Modeling of Critical Food Supply Chain Drivers Using DEMATEL Method and Blockchain Technology

The main purpose of this paper is to identify the critical drivers of the food supply chain (FSC) in the Indian context and find cause–effect relationships among the identified drivers using a decision-making trial and evaluation laboratory (DEMATEL)-based method. After a review of the literature and discussion with food chain experts, fourteen drivers have been identified for this study. Critical drivers and their causal relationships are explored through the cause-and-effect diagram. Results of this study show that the drivers namely "Shift towards a sustainable food system in India” (D7), "Social requirements on food security and safety” (D13), and "Growing attention towards food SCM amidst pandemic Covid-19” (D1) are the top three critical and influential drivers. It has been observed that limited research studies are done to identify and analyze the FSC drivers specific in the Indian context. Recent advancements in Blockchain technology have paved the path for improving the performance of the food supply chain with appropriate Blockchain technology intervention. Blockchain technology (BT) can be a new driver in the FSCM. This paper proposes a conceptual framework for the implementation of Blockchain technology in the food supply chain. This paper attempts to draw the attention of policymakers to develop a new sound policy with the help of Blockchain technology to ensure food security. © 2023, The Institution of Engineers (India).

Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the covid-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal is uses the sentiment analysis of twitter data relevant to covid-19 and classification of tweets with machine learning. © 2022 IEEE Computer Society. All rights reserved.

Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response

Most governments employ a set of quasi-standard measures to fight COVID-19, including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic response instrument is even more involved than anticipated. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision making and the differing national implementations of the response mechanism. In this article, through simple games, we show the effect of individual incentives on the decisions made with respect to mask wearing, social distancing, and vaccination, and how these may result in sub-optimal outcomes. We also demonstrate the responsibility of national authorities in designing these games properly regarding data transparency, the chosen policies, and their influence on the preferred outcome. We promote a mechanism design approach: It is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism;moreover, there is no one-size-fits-all solution when designing an effective solution. © 2022 held by the owner/author(s). Publication rights licensed to ACM.