Electrostatic Precipitator Design Optimization for the Removal of Aerosol and Airborne Viruses

In the midst of the COVID-19 pandemic, new requirements for clean air supply are introduced for heating, ventilation, and air conditioning (HVAC) systems. One way for HVAC systems to efficiently remove airborne viruses is by filtering them. Unlike disposable filters that require repeated purchases of consumables, the electrostatic precipitator (ESP) is an alternative option without the drawback of reduced dust collection efficiency in high-efficiency particulate air (HEPA) filters due to dust buildup. The majority of viruses have a diameter ranging from 0.1 μm to 5 μm. This study proposed a two-stage ESP, which charged airborne viruses and particles via positive electrode ionization wire and collected them on a collecting plate with high voltage. Numerical simulations were conducted and revealed a continuous decrease in collection efficiencies between 0.1 μm and 0.5 μm, followed by a consistent increase from 0.5 μm to 1 μm. For particles larger than 1 μm, collection efficiencies exceeding 90% were easily achieved with the equipment used in this study. Previous studies have demonstrated that the collection efficiency of suspended particles is influenced by both the ESP voltage and turbulent flow at this stage. To improve the collection efficiency of aerosols ranging from 0.1 μm to 1 μm, this study used a multi-objective genetic algorithm (MOGA) in combination with numerical simulations to obtain the optimal parameter combination of ionization voltage and flow speed. The particle collection performance of the ESP was examined under the Japan Electrical Manufacturers' Association (JEMA) standards and showed consistent collection performance throughout the experiment. Moreover, after its design was optimized, the precipitator collected aerosols ranging from 0.1 μm to 3 μm, demonstrating an efficiency of over 95%. With such high collection efficiency, the proposed ESP can effectively filter airborne particles as efficiently as an N95 respirator, eliminating the need to wear a mask in a building and preventing the spread of droplet infectious diseases such as COVID-19 (0.08 μm–0.16 μm). © 2023 by the authors.

A Simulation of a Design and Implementation of a New Low- Cost Prototype Portable Smart Ventilator

The outbreak of the Covid-19 virus prompted many engineers and researchers around the world to seek to develop mechanical ventilation devices and make them easy to use and affordable. This paper presents a simulation model for a group of medical sensors and gives very accurate results. This model contributes to the development and improvement of the artificial breathing system by comparing the results between the simulation model and the realistic response of the human lung. © 2023 IEEE.

Evaluating a Covid-19 Vaccine Centre in the UK Using a DES Model

Efficient management of a Covid-19 vaccine centre (VC) is necessary for proper-functioning of a mass vaccination programme. This study reports on an evaluation of the operational performance of a VC. There are two key considerations: the VC capacity (patients per hour) and the patient flow-time (total time patients spent in the centre). In this paper, Witness Horizon a simulation model tool that can be used to enhance the effectiveness of vaccination facilities is introduced. The model is developed using discrete event simulation. The model utilises animation whilst dynamically displaying key performance indicators. The uniqueness of this approach is the ability to simulate and analyse VC scenarios stochastically by varying hourly arrivals, walk-ins to drive-in ratios, staffing levels, registration, immunization, and observation capacities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

DEVS Model Design for Simulation Web App Deployment

The discrete-event system specification (DEVS) formalism has been recognized to be able to enable a formal and complete description of the components and subsystems of hybrid models. What is missing for accelerated adoption of DEVS-based methodology is to offer a way to design web apps to interact with a simulation model and to automatically deploy it on an online server which is remotely accessible from web app. The deployment of DEVS simulation models is the process of making models available in production where web applications, enterprise software, and APIs can consume the simulation by providing new inputs and generating outputs. This paper proposes a framework allowing one to simplify the DEVS simulation model building and deployment on the web by the modeling and simulation engineers with minimal web development knowledge. A case study on the management of COVID-19 epidemic surveillance is presented. © 2022 IEEE.

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.

Maritime Disruption Impact Evaluation Using Simulation and Big Data Analytics

Disruptions in maritime networks may cause significant financial burden and damage to business. Recently, some international ports have been experiencing unprecedented congestions due to the COVID19 pandemic and other disruptions. It is paramount for the maritime industry to further enhance the capability to assess and predict impacts of disruptions. With more data available from industrial digitization and more advanced technologies developed for big data analytics and simulation, it is possible to build up such capability. In this study, we developed a discrete event simulation model backed with big data analytics for realistic and valid inputs to assess impacts of the Suez Canal blockage to the Port of Singapore. The simulation results reveal an interesting finding that, the blockage occurred in the Suez Canal can hardly cause significant congestion in the Port of Singapore. The work can be extended to evaluate impacts of other types of disruptions, even occurring concurrently. © 2022 IEEE.

An agent-based model of COVID-19 dynamics during enhanced community quarantine: Exploring the role of food relief system in the presence of two SARS-CoV-2 variants

Introduction: The onset of the SARS-CoV-2 pandemic alerted the Philippine government to impose the enhanced community quarantine (ECQ) as a means to hamper human mobility and interaction and eventually diminish transmission. Due to severe limitations in accessibility to basic needs due to ECQ, the government devised amelioration programs. A year after the declaration of the SARS-CoV-2 pandemic, variants of concern were detected locally. Consequently, there is a necessity to prepare reinstatement of strict non-pharmaceutical interventions while meeting the food-related basic needs of the population. Studies related to food distribution during a strict community quarantine have been lacking. The importance of allocating provisions during extreme pandemic measures should be properly analyzed, especially when attempts had been made by local government units. Methods: This study devised an agent-based model (ABM) to observe the effects of the food relief system in mitigating the disease during Davao City ECQ when two variants are present in two adjacent villages. These relief distribution types are as follows: "regular and sufficient,” "regular but insufficient,” and "irregular” relief type. In total, three barangay scenarios were considered. Results and discussion: For the worst-case scenario, wherein a lot of infections are anticipated, the results show that the "irregular” relief type peaked at the highest number of cases, while the "regular and sufficient” relief type showed little to almost no new cases. The compromise-case scenario showed almost no difference between "regular but insufficient” and "regular and sufficient.” For the best-case scenario, the three relief types showed low average infected cases with almost small variance. The model was then compared, situationally, with Davao City barangays during ECQ and recommended which food relief type applies to the barangays. This could serve as a baseline on how food reliefs could be optimally distributed in cases where barangay conditions differently affect and transmit the SARS-CoV-2 virus of different variants with varying transmission rates within a community. Further development of the model should potentially be useful for decision support not only during pandemics but also in contexts where resource allocation to a community is involved. Copyright © 2023 Yap, Lachica, Paras, Panogalinog, Tubay and Mata.

Node Research on the Involvement of China's Carbon Tax Policy in the Context of COVID-19

The outbreak of COVID-19 provides a rare opportunity for the implementation of the carbon tax. To determine which stage is the most appropriate for introducing the policy, a simulation model based on China's panel data is established to analyze the impact of the carbon tax on government revenue and residents' income from five scenarios. A new GM-SD modeling method is proposed to ensure the accuracy of the model. The results show that the impact of the carbon tax on the government and the public is significantly different at different stages, and even the implementation of the carbon tax in the early stage of COVID-19 will reduce the government's tax revenue. The score analysis of government tax revenue, residents' surplus disposable income, residents' emotional value, and government administrative power finds that the middle period of COVID-19 is the best time to implement the policy. In addition, a more detailed analysis of five aspects, including total population, energy consumption, and national income, shows that the best time to implement the carbon tax policy is when the damage degree of COVID-19 is moderate. The analysis results can provide a reference and basis for China to introduce the carbon tax in the event of similar events as COVID-19, and have reference significance for other countries that have not implemented a carbon tax. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact of Vaccination Policies for COVID-19 Using Hybrid Simulation

A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.

Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19

COVID-19 related crimes like counterfeit Personal Protective Equipment (PPE) involve complex supply chains with partly unobservable behavior and sparse data, making it challenging to construct a reliable simulation model. Model calibration can help with this, as it is the process of tuning and estimating the model parameters with observed data of the system. A subset of model calibration techniques seems to be able to deal with sparse data in other fields: Genetic Algorithms and Bayesian Inference. However, it is unknown how these techniques perform when accurately calibrating simulation models with sparse data. This research analyzes the quality-of-fit of these two model calibration techniques for a counterfeit PPE simulation model given an increasing degree of data sparseness. The results demonstrate that these techniques are suitable for calibrating a linear supply chain model with randomly missing values. Further research should focus on other techniques, larger set of models, and structural uncertainty. © 2022 IEEE.

Epidemic Spread Modeling for COVID-19 Using Cross-fertilization of Mobility Data

We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different what-if counter-measure scenarios. IEEE

Simulation Model of a Multi-Hospital Critical Care Network

We develop a discrete event simulation model for a network of eight major intensive care units (ICUs) in British Columbia, Canada. The model also contains high acuity units (HAUs) that provide critical care to patients that cannot be cared for in a general medical ward, but do not require the full spectrum of care available in an ICU. We model patient flow within the ICU and HAU for each of the hospitals, as well as patient transfers to address ICU capacity. Included in the model is early discharge from ICU to HAU, sometimes called 'bumping', when the ICU is full, as well as ICU overflow beds. The simulation model, which is calibrated using the British Columbia Critical Care Database, will be used to support planning for critical care capacity under endemic and seasonal COVID-19. © 2022 IEEE.

Could Earlier Availability of Boosters and Pediatric Vaccines Have Reduced Impact of COVID-19?

The objective is to evaluate the impact of the earlier availability of COVID-19 vaccinations to children and boosters to adults in the face of the Delta and Omicron variants. We employed an agent-based stochastic network simulation model with a modified SEIR compartment model populated with demographic and census data for North Carolina. We found that earlier availability of childhood vaccines and earlier availability of adult boosters could have reduced the peak hospitalizations of the Delta wave by 10% and the Omicron wave by 42%, and could have reduced cumulative deaths by 9% by July 2022. When studied separately, we found that earlier childhood vaccinations reduce cumulative deaths by 2,611 more than earlier adult boosters. Therefore, the results of our simulation model suggest that the timing of childhood vaccination and booster efforts could have resulted in a reduced disease burden and that prioritizing childhood vaccinations would most effectively reduce disease spread. © 2022 IEEE.

Devices with Fuzzy Logic Control of Artificial Lung Ventilation

This article presents the development of a ventilator and its control algorithm. The main feature of the developed ventilator is compressed by a pneumatic drive. The control algorithm is based on the adaptive fuzzy inference system (ANFIS), which integrates the principles of fuzzy logic. The paper also presents a simulation model to test the designed control approach. The results of the experiment provide verification of the developed control system. The novelty of the article is, on the one hand, the implementation of the ANFIS controller, pressure control, with a description of the training process. On the other hand, in the article presented a draft ventilator with a detailed description of the hardware and control system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How to avoid a local epidemic becoming a global pandemic.

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Cost-utility analysis of the UPRIGHT intervention promoting resilience in adolescents.

BACKGROUND: As mental health in adulthood is related to mental status during adolescence, school-based interventions have been proposed to improve resilience. The objective of this study was to build a simulation model representing the natural history of mental disorders in childhood, adolescence and youth to estimate the cost-effectiveness of the UPRIGHT school-based intervention in promoting resilience and mental health in adolescence. METHODS: We built a discrete event simulation model fed with real-world data (cumulative incidence disaggregated into eight clusters) from the Basque Health Service database (609,381 individuals) to calculate utilities (quality-adjusted life years [QALYs]) and costs for the general population in two scenarios (base case and intervention). The model translated changes in the wellbeing of adolescents into different risks of mental illnesses for a time horizon of 30 years. RESULTS: The number of cases of anxiety was estimated to fall by 5,125 or 9,592 and those of depression by 1,269 and 2,165 if the effect of the intervention lasted 2 or 5 years respectively. From a healthcare system perspective, the intervention was cost-effective for all cases considered with incremental cost-utility ratios always lower than €10,000/QALY and dominant for some subgroups. The intervention was always dominant when including indirect and non-medical costs (societal perspective). CONCLUSIONS: Although the primary analysis of the trial did not did not detect significant differences, the UPRIGHT intervention promoting positive mental health was dominant in the economic evaluation from the societal perspective. Promoting resilience was more cost-effective in the most deprived group. Despite a lack of information about the spillover effect in some sectors, the economic evaluation framework developed principally for pharmacoeconomics can be applied to interventions to promote resilience in adolescents. As prevention of mental health disorders is even more necessary in the post-coronavirus disease-19 era, such evaluation is essential to assess whether investment in mental health promotion would be good value for money by avoiding costs for healthcare providers and other stakeholders.

The Impact of the COVID-19 Pandemic on Veterinary Clinical and Professional Skills Teaching Delivery and Assessment Format.

The limitations posed by the COVID-19 pandemic have been particularly challenging for courses teaching clinical and professional skills. We sought to identify how the COVID-19 pandemic has impacted the delivery of veterinary clinical and professional skills courses, including modifications to teaching and assessment, and to establish educators' perceptions of the efficacy of selected delivery methods. A branching survey was deployed to 35 veterinary schools in North America in March and April 2021. The survey collected data about curriculum and assessment in spring 2020, fall 2020, and spring 2021. Educators at 16 veterinary schools completed the survey (response rate: 46%). Educators quickly adapted curriculum to meet the requirements of their institutions and governments. Early in the pandemic (spring 2020), curriculum was delayed, delivered remotely, or canceled. Assessment methods frequently included virtual objective structured clinical examinations (OSCEs) and video-recorded skills assessments. Later in the pandemic (fall 2020, spring 2021), in-person clinical skills sessions resumed at many schools, often in smaller groups. Professional skills instruction typically remained virtual, as benefits were noted. Assessment methods began to normalize with in-person OSCEs resuming with precautions, though some school maintained virtual assessments. Educators noted some advantages to instructional methods used during COVID, including smaller group sizes, better prepared students, better use of in-person lab time, more focus on essential course components, provision of models for at-home practice, and additional educators' remote involvement. Following the pandemic, educators should consider retaining some of these changes while pursuing further advancements, including improving virtual platforms and relevant technologies.

A review on indoor airborne transmission of COVID-19– modelling and mitigation approaches

In the past few years, significant efforts have been made to investigate the transmission of COVID-19. This paper provides a review of the COVID-19 airborne transmission modeling and mitigation strategies. The simulation models here are classified into airborne transmission infectious risk models and numerical approaches for spatiotemporal airborne transmissions. Mathematical descriptions and assumptions on which these models have been based are discussed. Input data used in previous simulation studies to assess the dispersion of COVID-19 are extracted and reported. Moreover, measurements performed to study the COVID-19 airborne transmission within indoor environments are introduced to support validations for anticipated future modeling studies. Transmission mitigation strategies recommended in recent studies have been classified to include modifying occupancy and ventilation operations, using filters and air purifiers, installing ultraviolet (UV) air disinfection systems, and personal protection compliance, such as wearing masks and social distancing. The application of mitigation strategies to various building types, such as educational, office, public, residential, and hospital, is reviewed. Recommendations for future works are also discussed based on the current apparent knowledge gaps covering both modeling and mitigation approaches. Our findings show that different transmission mitigation measures were recommended for various indoor environments;however, there is no conclusive work reporting their combined effects on the level of mitigation that may be achieved. Moreover, further studies should be conducted to understand better the balance between approaches to mitigating the viral transmissions in buildings and building energy consumption. © 2022

A Simulation Model for Predicting the Spread of COVID-19 Virus

COVID-19 has shown a high potential of transmission within the last two years. To interrupt the chain of transmission, it is estimated that 85% of the population must be immune. Since not all society wants to take vaccinations, it is very important to predict how the current precautions will impact the virus development. This paper presents a simulation model framework that can be used to predict the development of SARS-CoV-2 virus. The model was based on SEIR (Susceptible-Exposed-Infectious-Removed) model but was significantly extended by adding a set of additional changeable parameters and a new layer responsible for modelling the virus spread patterns. To test the capability of the model to predict the virus spread in a hermetic group of people, we run the 28-days simulation of the spread of the 4-th wave of COVID-19 in a shopping mall visited by 6500 agents. The simulation results showed a remarkable relation to the real development of the 4th wave of COVID-19 in a small hermetic community (the gryfi ' nski district in West Pomeranian Voivodeship of Poland).

Innovative use of a flipped-classroom approach to teach fundamental nursing skills.

Prelicensure nursing students are required to master fundamental nursing skills. The COVID-19 pandemic created challenges in maintaining excellence while teaching skill acquisition. The purpose of this study was to evaluate skill validation scores and student satisfaction and self-confidence using a flipped classroom approach and a low-fidelity simulation model to innovatively teach skill acquisition. Researchers used a quasi-experimental method to compare skill validation scores of a control group and intervention group using independent samples t-test. Researchers also evaluated whether prelicensure nursing students had satisfaction and self-confidence with this teaching strategy. Findings suggested that skills validations scores were no different using a flipped-classroom approach than in-person instruction. Prelicensure nursing students were satisfied and self-confident following the implementation of this teaching strategy. This teaching strategy has the potential to decrease in-person clinical practice time, provide alternative opportunities for clinical make-up and remediation, and decrease cost.