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The purpose of this study was to examine how data from the World Health Organization, United States Environmental Protection Agency and Center for Disease Control have evolved with relation to engineering controls for heating, ventilation and air-conditioning (HVAC) systems to mitigate the spread of spread of aerosols (specifically related to the COVID-19 pandemic) in occupied buildings.

A document analysis of the pandemic-focused position documents from the aforementioned public health agencies and national HVAC authorities was performed. This review targeted a range of evidence from recommendations, best practices, codes and regulations and peer-reviewed publications and evaluated how they cumulatively evolved over time. Data was compared between 2020 and 2021.

This research found that core information provided early in the pandemic (i.e. early 2020) for engineering controls in building HVAC systems did not vary greatly as knowledge of the pandemic evolved (i.e. in June of 2021). This indicates that regulating agencies had a good, early understanding of how airborne viruses spread through building ventilation systems. The largest evolution in knowledge came from the broader acceptance of building ventilation as a transmission route and the increase in publications and ease of access to the information for the general public over time.

The promotion of the proposed controls for ventilation in buildings, as outlined in this paper, is another step toward reducing the spread of COVID-19 and future aerosol spread viruses by means of ventilation.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

Biocontaminants represent higher risks to occupants' health in shared spaces. Natural ventilation is an effective strategy against indoor air biocontamination. However, the relationship between natural ventilation and indoor air contamination requires an in-depth investigation of the behavior of airborne infectious diseases, particularly concerning the contaminant's viral and aerodynamic characteristics. This research investigates the effectiveness of natural ventilation in preventing infection risks for coronavirus disease (COVID-19) through indoor air contamination of a free-running, naturally-ventilated room (where no space conditioning is used) that contains a person having COVID-19 through building-related parameters.

This research adopts a case study strategy involving a simulation-based approach. A simulation pipeline is implemented through a number of design scenarios for an open office. The simulation pipeline performs integrated contamination analysis, coupling a parametric 3D design environment, computational fluid dynamics (CFD) and energy simulations. The results of the implemented pipeline for COVID-19 are evaluated for building and environment-related parameters. Study metrics are identified as indoor air contamination levels, discharge period and the time of infection.

According to the simulation results, higher indoor air temperatures help to reduce the infection risk. Free-running spring and fall seasons can pose higher infection risk as compared to summer. Higher opening-to-wall ratios have higher potential to reduce infection risk. Adjacent window configuration has an advantage over opposite window configuration. As a design strategy, increasing opening-to-wall ratio has a higher impact on reducing the infection risk as compared to changing the opening configuration from opposite to adjacent. However, each building setup is a unique case that requires a systematic investigation to reliably understand the complex airflow and contaminant dispersion behavior. Metrics, strategies and actions to minimize indoor contamination risks should be addressed in future building standards. The simulation pipeline developed in this study has the potential to support decision-making during the adaptation of existing buildings to pandemic conditions and the design of new buildings.

The addressed need of investigation is especially crucial for the COVID-19 that is contagious and hazardous in shared indoors due to its aerodynamic behavior, faster transmission rates and high viral replicability. This research contributes to the current literature by presenting the simulation-based results for COVID-19 as investigated through building-related and environment-related parameters against contaminant concentration levels, the discharge period and the time of infection. Accordingly, this research presents results to provide a basis for a broader understanding of the correlation between the built environment and the aerodynamic behavior of COVID-19.

Physical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed.

This paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world.

Results show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation.

This research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues?

This study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.

The purpose of this study is to conduct a rigorous systematic literature review and present a summary of building systems and technologies that can be used to mitigate the spread of airborne viruses. With the recent outbreak of COVID-19, occupants’ health and indoor air quality (IAQ) have become a critical issue for facility managers to maintain the full functionality of the buildings. An improved understanding of these available systems will help facility managers and building owners to protect the health and safety of building occupants.

The PRISMA protocol was used for defining the literature search methodology. The concept mapping technique was used for determining the keywords. The keywords were then used to search for relevant articles using the Scopus database and Google Scholar. A thorough bibliometric analysis and qualitative analysis were conducted for the selected publications.

It was found that sensor technologies, botanical air-filtration systems and artificial intelligence could be used to effectively monitor and improve IAQ. In addition, natural ventilation is one of the low-cost and effective methods of reducing contaminants from the indoor air. Computational fluid dynamic modeling can be used to understand the flow of virus particles within the building through the heating, ventilation and air-conditioning (HVAC) system. Several changes to the HVAC system are also discussed.

This study contains a diversity of methods from the existing literature that were systematically selected to present the state-of-the-art building systems and technologies that can effectively improve IAQ. The researchers plan to follow up on the findings of this research and will conduct an empirical study to assess its impact on IAQ.

The paper aims to apply the probabilistic analysis of risks, improve the prediction and control of infections and optimise the use of resources and the knowledge available at all times.

First, a model based on Bayesian inference, which can be solved with the WinBUGS (Windows interface Bayesian inference Using Gibbs Sampling) simulation software, is described to reduce the uncertainty of the parameter that most influences air transmission: the rate of quanta emitted by the infected. Second, a method for predicting the expected number of infections and combining available resources to reduce parameter is described.

The results indicate that it is possible to initiate a powerful learning process when all available knowledge is integrated alongside the newly observed data and that it is possible to quantify the interaction between the environment and the spaces, improving the communication process by providing the values in a format that facilitates the objective perception of danger.

The implementation of the inference model requires access to the spaces where there were infected.

The current study provides a model and a method to improve the probabilistic analysis of risks, which allows the systematisation of the risk-based management approach to control community transmission caused by infectious agents that use the airway.

The application of the risk assessment and treatment method requires collaboration between the parties that will help the effective implementation of the improvements, such as to verify whether the available resources are sufficient to achieve control.

A hierarchical Bayesian inference model is presented to control the uncertainty in the quanta rate. Bayesian inference initiates a learning process to better understand random uncertainty. A method to quantify and communicate risk was also presented, which proposes to decompose the risk into four components to predict the expected number of infected individuals, helping to implement improvement measures, with the resources and knowledge available.

As the world faces a new health crisis threatening people with the spread of Covid-19, this study aims to summarize the key information of Covid-19 related to disease characteristics, diagnosis, treatment and prevention along with the lessons learned from Thailand.

The narrative review was synthesized from various sources such as the World Health Organization; Centers for Disease Control and Prevention; Ministry of Public Health and other related news; articles in ScienceDirect, PubMed, Google Scholar; and the author's perspective regarding the lessons learned from Thailand with keywords of “Covid-19” and “Coronavirus” from January to August 2020. Google Trends was used to set common questions.

Covid-19 is the seventh family of coronaviruses that cause various symptoms related to respiratory systems. The disease can be treated through general and symptomatic treatment, by using antiviral drugs. As of July 2020, there are four potential vaccine candidates ChAdOx1 nCoV-19, mRNA-1273, Ad5-nCOV and BNT162b1. The recommendations for Covid-19 prevention are physical distancing, face masks, eye protection and hand washing. Thailand is now considered as low-risk for Covid-19 possibly because of (1) soft policy by government actions, (2) village health volunteers, (3) integration of technology and (4) fact-based communications.

This study summarized the key points about Covid-19, clarified some misunderstandings and shared strategic actions from Thailand, which can be adapted according to the different capacities and situations in other countries.

Purpose – The authors examine framing and narrativization in news coverage of health threats to assess variations in news discourse for known, emerging and novel health risks. Methodology/Approach – Using the analytical categories of known, emerging, and novel risks the authors discuss media analyses of anti-vaccination, antimicrobial resistance (AMR), and Covid-19. Findings – Known risks are framed within a biomedical discourse in which scientific evidence underpins public health guidelines, and following these directives prevent risk exposure while non-compliance is characterized as immoral and risky. News coverage of emerging risks highlights public health guidelines but fails to convey their importance as the risks seem too distant or abstract. Media coverage of novel risks is characterized by the ubiquity of uncertainty, which emerges as a “master frame” under which all incidents and events are subsumed. Stories about novel risks highlight the fluid and changing nature of scientific knowledge, which has the unintended effect of fueling uncertainty as studies and experts contradict each other. Originality/Value – This chapter introduces a new analytical framework for examining how media stories represent public health risks, along with previously unpublished analysis of media coverage about AMR and Covid-19. This chapter provides insight about the nature of risk discourses involving media, public health officials, activists, and citizens.

Due to the ongoing Covid-19 pandemic, ventilation in a small cabin where social distancing cannot be guaranteed is extremely important. This study aims to find out the best configuration of open and closed windows in a moving car at varying speeds to improve the ventilation efficiency. The effectiveness of other mitigation measures including face masks, taxi screens and air conditioning (AC) systems are also evaluated.

Each window is given three opening levels: fully open, half open and fully closed. For a car with four windows, this yields 81 different configurations. The location of virus source is also considered, either emitting from the driver or from the rear seat passenger. Then three different travelling speeds, 5 m/s, 10 m/s and 15 m/s, are examined for the window opening/closing configurations that provide the best ventilation effect. A study into the effectiveness of face masks is realised by adjusting virus injection amounts; and the simulation of taxi screens and AC system simply requires a small modification to the car model.

The numerical studies identify the top window opening/closing configurations that provide the most efficient ventilation at different moving speeds, along with a comprehensive ranking list. The results show that fully opening all windows is not always the best choice. Simulations evaluating other mitigation measures confirm good effect of face masks and poor performance of taxi screens and AC systems.

This work is the first large-scale numerical simulation and parametric study about different window opening/closing configurations of a moving car. The results provide useful guides for travellers in shared cars to mitigate Covid-19 transmission risks. The findings are helpful to both individuals' health and society's recovery in the Covid-19 era and they also provide useful information to protect people from other respiratory infectious diseases such as influenza.

Festivals and events have been regarded as one of the sources of super-spreading of the COVID-19 virus. This industry has higher vulnerability to external factors. Against this backdrop, this chapter explores the various methods that will define the future for them while promoting the ideas of sustainability.