Modeling and simulation of infectious diseases: Microscale transmission, decontamination and macroscale propagation

The COVID-19 pandemic that started in 2019-2020 has led to a gigantic increase in modeling and simulation of infectious diseases. There are numerous topics associated with this epoch-changing event, such as (a) disease propagation, (b) transmission, (c) decontamination, and (d) vaccines. This is an evolving field. The targeted objective of this book is to expose researchers to key topics in this area, in a very concise manner. The topics selected for discussion have evolved with the progression of the pandemic. Beyond the introductory chapter on basic mathematics, optimization, and machine learning, the book covers four themes in modeling and simulation infectious diseases, specifically: Part 1: Macroscale disease propagation, Part 2: Microscale disease transmission and ventilation system design, Part 3: Ultraviolet viral decontamination, and Part 4: Vaccine design and immune response. It is important to emphasize that the rapid speed at which the simulations operate makes the presented computational tools easily deployable as digital twins, i.e., digital replicas of complex systems that can be inexpensively and safely optimized in a virtual setting and then used in the physical world afterward, thus reducing the costs of experiments and also accelerating development of new technologies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Nonlinear dynamics modeling and epidemic forecast of COVID-19

To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.

Simulation Analysis of COVID-19 Epidemic Model in Wuhan Based on SIR Model

Based on SIR model, combined with the mode of COVID-19 epidemic spread in Wuhan, the SIR model of COVID-19 epidemic spread is constructed, which mainly includes three aspects: simulation of the average number of infected people in COVID-19, simulation of cross-infection in COVID-19 and simulation of contact infection in COVID-19. Using the results of these three simulations, we can predict the spread of COVID-19 epidemic in the region, and find out the methods to prevent and control the outbreak or spread of the epidemic. © 2023 SPIE.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

Numerical Simulation of the COVID-19 Herd Immunity Based on Complex Network Modeling

Constructing an epidemic dynamic model and exploring the spreading law of epidemic have very important theoretical significance for epidemic prevention and control. Based on the existing homogeneous mixing model, in view of the increasingly obvious heterogeneity of individual contact relationships, and each individual is in a different contact relationship, a dynamic small-world network model that takes into account individual status. Contact tracking has been established to simulate the spread of the COVID-19 in society. By comparing the simulation results, the rationality of the built model is explained. On this basis, the simulation calculated the impact of the network topology and the proportion of vaccinated people on the spread of the COVID-19, analyzed the critical value of herd immunity. The established propagation model is reasonable, and feasible to achieve herd immunization by vaccination. © 2023 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Development of a Web Application for Telecommuting Capability Assessment Embedded with Fuzzy Model

By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.

Revisiting online academic networks within the COVID-19 pandemic – From the intellectual capital of knowledge networks towards institutional knowledge capitalization

Purpose: The study sets out to explore the mediating role of intellectual capital (IC) dimensions (i.e. human, structural and relational) between scholars' affiliation to online academic networks and institutional knowledge capitalization. Online academic networks are tackled through the lens of knowledge networks which have been of primary importance for new relevant knowledge acquisition during the COVID-19 pandemic. Design/methodology/approach: A questionnaire-based survey of 305 academics from 35 different countries was conducted from July to December 2021, employing a partial least squares structural equation modelling technique. The database was initially filtered to ensure the adequacy of the sample, and data were analyzed using the statistics software package SmartPLS 3.0. Findings: Evidence was brought forward that the proposed conceptual model accounted for 52.5% of the variance in institutional knowledge capitalization, the structural and relational capital availed by knowledge networks exerting strong positive influence on the dependent variable. Research limitations/implications: The study has both research and managerial implications in that it approaches a topical phenomenon, namely the capitalization of online academic networks in the COVID-19 context, which has dramatically altered the way that research and teaching are conducted worldwide. Originality/value: The most important contribution of the paper resides in the comprehensive research model advanced which covers individual, organizational and network multifaced layers, starting with the personal and institutional motives to join a specialized network, continuing with the opportunities provided by knowledge networks in terms of intellectual capital harnessing, and ending with its influence on higher education organizations. [ FROM AUTHOR] Copyright of Journal of Intellectual Capital is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

FINANCIAL SECURITY in SMALL BUSINESS in PERIOD of DIGITAL ECONOMY: In CASE of UZBEKISTAN

In this paper has been proposed a methodology for ensuring the financial security of enterprises in the context of recession caused by the COVID-19 pandemic. Based on pre-crisis data related to the new coronavirus infection pandemic and multi-component modeling of the dynamics of industrial production in the Republic of Uzbekistan during the "corona crisis,"this study seeks to identify the dynamics of growth by economic activity type and recovery rate in order to identify areas of state support for industrial production. In this paper has been investigated issues of financial security management of textile enterprises. On the basis of secondary statistics, the growth of textile production in the regions of the Republic of Uzbekistan in 2008-2020 was analyzed and the factors influencing it were identified. By the author have been presented the main tasks and conditions for the financial security of enterprises, as well as developed scientific and practical recommendations for eliminating factors affecting the financial security of textile enterprises. © 2022 Owner/Author.

Analyzing the research trends of COVID-19 using topic modeling approach

PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction

This study proposes a graph convolutional neural networks (GCN) architecture for fusion of radiological imaging and non-imaging tabular electronic health records (EHR) for the purpose of clinical event prediction. We focused on a cohort of hospitalized patients with positive RT-PCR test for COVID-19 and developed GCN based models to predict three dependent clinical events (discharge from hospital, admission into ICU, and mortality) using demographics, billing codes for procedures and diagnoses and chest X-rays. We hypothesized that the two-fold learning opportunity provided by the GCN is ideal for fusion of imaging information and tabular data as node and edge features, respectively. Our experiments indicate the validity of our hypothesis where GCN based predictive models outperform single modality and traditional fusion models. We compared the proposed models against two variations of imaging-based models, including DenseNet-121 architecture with learnable classification layers and Random Forest classifiers using disease severity score estimated by pre-trained convolutional neural network. GCN based model outperforms both imaging-only methods. We also validated our models on an external dataset where GCN showed valuable generalization capabilities. We noticed that edge-formation function can be adapted even after training the GCN model without limiting application scope of the model. Our models take advantage of this fact for generalization to external data. © 2023 IEEE.

Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis of Covid-19

Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.

Factors Affecting Consumer Behaviour during COVID-19:A Case Study in Bangladesh

Customer shopping behaviour has changed and people are becoming used to accessing, using and adapting to online shopping rather than visiting stores physically due to COVID-19 restrictions. It is not known how long the trend will last but it can be observed that there will be changes in current and future models in almost every business around the world. According to the 'Motivation-need theory' (1943), every individual considers five (5) key elements to fulfil their needs. It includes physiological survival, safety, love, esteem, and self-actualization. The big question is why consumers act differently during the global pandemic, which does not support Maslow's 'Motivation-need theory'. It might be the panic situation all over the world, frustration of losing jobs, mental stress while isolated and many other factors that are making consumers act differently while shopping from e-commerce or different social media platform. This research study aims to examine the factors affecting consumer behaviour toward online purchasing during COVID-19 in Bangladesh. . © 2023 IEEE.

Molecular Dynamics and Free Energy Landscape Study of SARS-CoV-2 Omicron M-pro Boswellic acid Compounds of Boswellia Serrata Inhibitors

This work sheds light on the effect of boswellic acid compounds (Alpha boswellic acid, Beta boswellic acid, 11-keto beta boswellic acid and 3-Acetyl-11-keto beta boswellic acid) upon inhibiting SARS-CoV-2 M-pro and O-M-pro (Main protease). A good docking score (-8.4 kcal/mol) is found in the case of 3-Acetyl-11-keto beta boswellic acid as compared to the reference and three other boswellic acid compounds. ADMET results suggest that all these compounds are nontoxic and their pharmacokinetic properties are satisfactory. Moreover, a stability analysis with M-pro/O-M-pro through RMSD, RMSF, hydrogen bonds and Rg parameters in MD simulations is made and we found better values than the reference case. Pre and post-MD structures of Ligands-M-pro show a similar binding site whereas a drift can be noted for L-O-M-pro. 3-Acetyl-11-keto beta boswellic acid shows an average of five hydrogen bonds and it remains stable within the binding pocket of M-pro during the simulation period in comparison to other boswellic acids compounds. Various metastable conformations are observed for all compounds in FEL (free energy landscape), however, Acyclovir-M-pro, Alpha boswellic acid-M-pro and Beta boswellic acid-O-M-pro display only one global minimum. The results suggest that these compounds can be used as potential lead molecules for breakthroughs in drug discovery.

Modeling of Physical Activity Behavioral Interventions Relying on MPC Strategy

Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.

Verification and validation of a framework for collaborative BIM implementation, measurement and management (CIMM)

PurposeThe purpose of this research is to develop through a two-stage verification and validation process a novel implementation framework for collaborative BIM, utilising experts from academia and industry as well as a real-world case study project.Design/methodology/approachThe aim of this research was to build upon previous research findings by the authors in order to develop an implementation framework that stems from ousting the inefficiencies of current collaborative BIM practices. This is achieved by a more objectified and quantified approach towards seeking heightened transparency and objectivism of what is required through the implementation of BIM. The mixed research methods technique of both qualitative and quantitative data collection was utilised, with the structure consisting of a two-stage approach utilising the Delphi model for verification and validation. This was developed to test the novelty and beneficial structure hypothesis involving 15 core BIM experts from academia, construction and design with c. 22 years average experience. Validation was undertaken on a complex, high value real world building structures project in central London, inclusive of 8 core project BIM experts. The research utilised a developed solution that mirrored and provided a more holistic representation guiding the practitioners as a project team step by step through the determination of underpinning elements, which support the goal of enhanced information requirements as well as executing the prioritisation measurement tools as part of the framework. Data ascertained at the workshop case study prioritised areas of importance that are core in supporting the delivering of these enhanced information requirements at a project delivery level, which were in order of prioritisation determined by the project team (1) constraints (39.17%), (2) stakeholder requirements (35.78%), (3) coordination (existing asset) (15.86%), (4) exchange requirements (5.38%) and (5) level of information need (3.81%). Furthermore, risk mitigations for the top three priorities were focussed on early stakeholder engagement, appropriation of survey data collection, focus on quality of outputs and applying toolsets and processes with meaning and emphasis on the defined high-level requirements.FindingsFindings show that the framework and the developed solution translate the process methodology of the framework schema into a useable and beneficial tool that provides both qualitative and quantitative inputs and outputs. Furthermore, a collective agreement on the objectives, risk mitigations and assignment of tasks in order to achieve outcomes is presented, with evidence on numerical weightings and goal achievement.Research limitations/implicationsDue to the impacts of COVID-19 on physical engagements both the verification (electronic survey questionnaire) and validation (case study project) were undertaken remotely, using available technologies and web interfaces.Practical implicationsThe case study workshop was limited to one building structures project in central London of a value of c. £70 m design and build cost that the project team (participants) were actively engaged with.Social implicationsThe social impacts of this research has resulted in the review of existing systems, methods and approaches from a wider perspective of theoretical and applied environments, which led to the development of a novel approach and framework guided by an interactive and useable solution.Originality/valueAs shown within the core findings, experts across academia and industry (design and construction) confirmed that the framework methodology and application were 100% novel, and added a benefit to the existing collaborative BIM approach. Value added is that through objectifying, weighting/prioritizing and creating a discussion supported by qualitative and quantitative reasoning the focus on what collaborative BIM is to achieve is increased, and thus the likelihood of successful implementation.

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.

Application of Fractional SIQRV Model for SARS-CoV-2 and Stability Analysis

Throughout history, infectious diseases have been the cause of outbreaks and the deaths of people. It is crucial for endemic disease management to be able to forecast the number of infections at a given moment and the frequency of new infections so that the appropriate precautions can be taken. The COVID-19 pandemic has highlighted the value of mathematical modeling of pandemics. The susceptible–infected–quarantined–recovered–vaccinated (SIQRV) epidemic model was used in this work. Symmetrical aspects of the proposed dynamic model, disease-free equilibrium, and stability were analyzed. The symmetry of the population size over time allows the model to find stable equilibrium points for any parameter value and initial conditions. The assumption of the strong symmetry of the initial conditions and parameter values plays a key role in the analysis of the fractional SIQRV model. In order to combat the pandemic nature of the disease, control the disease in the population, and increase the possibility of eradicating the disease, effective control measures include quarantine and immunization. Fractional derivatives are used in the Caputo sense. In the model, vaccination and quarantine are two important applications for managing the spread of the pandemic. Although some of the individuals who were vaccinated with the same type and equal dose of vaccine gained strong immunity thanks to the vaccine, the vaccine could not give sufficient immunity to the other part of the population. This is thought to be related the structural characteristics of individuals. Thus, although some of the individuals vaccinated with the same strategy are protected against the virus for a long time, others may become infected soon after vaccination. Appropriate parameters were used in the model to reflect this situation. In order to validate the model, the model was run by taking the COVID-19 data of Türkiye about a year ago, and the official data on the date of this study were successfully obtained. In addition to the stability analysis of the model, numerical solutions were obtained using the fractional Euler method. © 2023 by the authors.

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.