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The COVID-19 epidemic has required countries to implement different containment strategies to limit its spread, like strict or weakened national lockdown rules and the application of age-stratified vaccine prioritization strategies. These interventions have in turn modified the age-dependent patterns of social contacts. In our recent paper, starting from the available age-structured real data at the national level, we identified, for the Italian case, specific virulence parameters for a two-age-structured COVID-19 epidemic compartmental model (under 60, and 60 years and over) in six different diseases transmission scenarios under concurrently adopted feedback interventions. An interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19 disease has been accordingly provided. In this paper, which can be viewed as a sequel to the previous one, we mainly apply the same general methodology therein (involving the same dynamic model) to new data covering the three subsequent additional scenarios: (i) a mitigated coordinated intermittent regional action in conjunction with the II vaccination phase; (ii) a super-attenuated coordinated intermittent regional action in conjunction with the II vaccination phase; and (iii) a last step towards normality in conjunction with the start of the III vaccination phase. As a new contribution, we show how meaningful updated information can be drawn out, once the identification of virulence parameters, characterizing the two age groups within the latest three different phases, is successfully carried out. Nevertheless, differently from our previous paper, the global optimization procedure is carried out here with the number of susceptible individuals in each scenario being left free to change, to account for reinfection and immunity due to vaccination. Not only do the slightly different estimates we obtain for the previous scenarios not impact any of the previous considerations (and thus illustrate the robustness of the procedure), but also, and mainly, the new results provide a meaningful picture of the evolution of social behaviors, along with the goodness of strategic interventions. [ABSTRACT FROM AUTHOR]

In this paper, we examine the epidemiological model B-SIR, focusing on the dynamic law that governs the transmission rate B. We define this dynamic law by the differential equation B ′ / B = F ⊕ − F ⊖ , where F ⊖ represents a reaction factor reflecting the stress proportional to the active group's percentage variation. Conversely, F ⊕ is a factor proportional to the deviation of B from its intrinsic value. We introduce the notion of contagion impulse f and explore its role within the model. Specifically, for the case where F ⊕ = 0 , we derive an autonomous differential system linking the effective reproductive number with f and subsequently analyze its dynamics. This analysis provides new insights into the model's behavior and its implications for understanding disease transmission. [ABSTRACT FROM AUTHOR]

In this paper, an SEAI epidemic model with asymptomatic infection is studied under the background of mass transmission of COVID-19. First, we use the next-generation matrix method to obtain the basic reproductive number R 0 and calculate the equilibrium point. Secondly, when R 0 < 1 , the local asymptotic stability of the disease-free equilibrium is proved by Hurwitz criterion, and the global asymptotic stability of the disease-free equilibrium is proved by constructing the Lyapunov function. When R 0 > 1 , the system has a unique endemic equilibrium point and is locally asymptotically stable, and it is also proved that the system is uniformly persistent. Then, the application of optimal control theory is carried out, and the expression of the optimal control solution is obtained. Finally, in order to verify the correctness of the theory, the stability of the equilibrium point is numerically simulated and the sensitivity of the parameters of R 0 is analyzed. We also simulated the comparison of the number of asymptomatic infected people and symptomatic infected people before and after adopting the optimal control strategy. This shows that the infection of asymptomatic people cannot be underestimated in the spread of COVID-19 virus, and an isolation strategy should be adopted to control the spread speed of the disease. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease that poses a serious risk to public health worldwide. To reduce its spread, people need to adopt preventive behaviours such as wearing masks, maintaining physical distance, and isolating themselves if they are infected. However, the effectiveness of these measures may depend on various factors that differ across countries. This paper investigates how some factors, namely outsiders' effect, life expectancy, population density, smoker percentage, and temperature, influence the transmission and death rate of COVID-19 in ninety-five top-affected countries. We collect and analyse the data of COVID-19 cases and deaths using statistical tests. We also use fuzzy logic to model the chances of COVID-19 based on the results of the statistical tests. Unlike the conventional uniform weighting of the rule base in fuzzy logic, we propose a novel method to calculate the weights of the rule base according to the significance of the factors. This study aims to provide a comprehensive and comparative analysis of the factors of COVID-19 transmission and death rates among different countries. [ABSTRACT FROM AUTHOR]

COVID-19 has become a pandemic which has resulted in measures being taken for the health and safety of people. The spreading of this disease is particularly evident in indoor spaces, which tend to get overcrowded with people. One such place is the airport where a plethora of passengers gather in common places, such as coffee shops and duty-free shops as well as toilets and gates. Guiding the passengers to less overcrowded places within the airport may be a solution to reduce disease spread. In this paper, we suggest a passenger routing algorithm whereby the passengers are guided to less crowded places by using a weighting factor, which is minimised to accomplish the desired goal. We modeled a number of shops in an airport using the AnyLogic software and we tested the algorithm showing that the exposure time is less with routing and that people are appropriately spread out across the common spaces, thus preventing overcrowding. Finally, we added a real airport in Kavala, Greece to show the efficiency of our approach. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought to the forefront the importance of predicting and controlling an epidemic outbreak with policies such as vaccination or reducing social contacts. This paper studies an SIHR epidemic model characterized by susceptible (S), non-seriously infected (I), hospitalized (H), and recovered (R) subpopulations, and dynamic vaccination; vaccination itself and H are fed back, and its dynamics are also determined by a free-design time-dependent function and parameters. From a theoretical analysis, the well-posedness of the model is demonstrated; positivity and the disease-free ( P d f ) and endemic ( P e e ) equilibrium points are analyzed. The controlled reproduction number ( R c ) is proved to be a threshold for the local asymptotic stability of  P d f  and the existence  P e e ; when  R c < 1  ( R c > 1 ), then  P d f  is (not) locally asymptotically stable and  P e e  does not (does) exist. Simulations have been carried out with data concerning COVID-19 where the importance of keeping  R c < 1  to prevent the disease spreading and future deaths is highlighted. We design the control input, since it can be easily adapted to match the user specification, to obtain impulsive and regular vaccination and fulfill the condition  R c < 1 . [ABSTRACT FROM AUTHOR]

Dental aerosol-generating procedures produce a large amount of splatters and aerosols that create a major concern for airborne disease transmission, such as COVID-19. This study established a method to visualise splatter and aerosol contamination by common dental instrumentation, namely ultrasonic scaling, air-water spray, high-speed and low-speed handpieces. Mock dental procedures were performed on a mannequin model, containing teeth in a typodont and a phantom head, using irrigation water containing fluorescein dye as a tracer. Filter papers were placed in 10 different locations to collect splatters and aerosols, at distances ranging from 20 to 120 cm from the source. All four types of dental equipment produced contamination from splatters and aerosols. At 120 cm away from the source, the high-speed handpiece generated the greatest amount and size (656 ± 551 μm) of splatter particles, while the triplex syringe generated the largest amount of aerosols (particle size: 1.73 ± 2.23 μm). Of note, the low-speed handpiece produced the least amount and size (260 ± 142 μm) of splatter particles and the least amount of aerosols (particle size: 4.47 ± 5.92 μm) at 120 cm. All four dental AGPs produce contamination from droplets and aerosols, with different patterns of distribution. This simple model provides a method to test various preventive strategies to reduce risks from splatter and aerosols. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic impacted many if not all aspects of our lives, including the way we handle money. This paper takes mobile payments as an example to show how COVID-19 affected the United Arab Emirates. Many researchers rushed into writing their first impressions in response to the pandemic to produce early study results. This paper uses these early electronic articles and blogs as valuable data sources for a first-level analysis. It attempts to assess the impact of COVID-19 on mobile payment services in the post-pandemic world from the demand and supply perspective in the UAE. The previous literature has been reviewed to understand the scope of the services present in the UAE before the onset of the pandemic. Further, an online questionnaire with open- and closed-ended questions was used to gather data from 125 vendors capable of receiving mobile payments. Regression analysis with two time periods has been included to develop a better perception of mobile payment services before and after the spread of the coronavirus. It was found that COVID-19 has created a suitable environment for mobile payments, with significant positive relationship in both periods. The reasons for using mobile payments during the pandemic were to apply social distancing and to avoid physical touch, a requirement in the COVID-19 period, resulting in a move towards creating a positive attitude towards a cashless society. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of "two types of control", namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government's ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China's political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., "infection–treatment–immunization", thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict "zeroing" and "dynamic zeroing" policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to "coexist" with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., "relaxation–suppression–relaxation–suppression", and began to "coexist" with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of "medical control" and "social control". [ABSTRACT FROM AUTHOR]

The emergence of SARS-CoV-2 virus mutations is a pressing concern in the context of the COVID-19 pandemic. In this paper, a Susceptible–Infected–Recovered (SIR) compartmental model has been formulated in terms of cellular automata and ordinary differential equations to simulate the transmission dynamics of a virus. This model accounts for the potential of new virus mutations to create additional infections of a multi-strain disease while also considering the impact of vaccination on disease control within a population. The basic reproduction number of the disease is derived, and the effect of virus mutations and vaccination rates is evaluated on a population over five years. The results of numerical simulations demonstrate the significant role of maintaining high vaccination rates in controlling the spread of the virus, even when assuming that all variants have similar illness characteristics and that a single shot of vaccine provides complete and lifelong protection against all strains. The findings underscore the necessity for countries to implement a consistent and high-level vaccination plan as soon as vaccines become available in order to mitigate the impact of a pandemic effectively. In conclusion, this study highlights the importance of taking into account the potential impact of virus mutations for controlling the COVID-19 pandemic. Furthermore, it emphasizes the critical role of vaccination in limiting the spread of the virus, and emphasizes the need to implement and maintain high vaccination rates as part of a comprehensive approach to managing the ongoing pandemic. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO 2 concentration. The validation results show that the model can predict room temperature and CO 2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller. [ABSTRACT FROM AUTHOR]

Featured Application: Intelligent prevention and control of infectious diseases. Infectious diseases, such as COVID-19, may have a significant impact on human daily life and social activities. One effective method to prevent and control the spread of such diseases is to accurately locate the sources of infection and limit the possible exposure to the virus. This paper presents a method, system, big data storage and analysis to control the infection based on Bluetooth technology. GPS and Bluetooth positioning are combined to track the movement trajectory of each person with a smart device, locally store the location information and close contacts, as well as periodically update it on the cloud platform. Based on the related algorithms of big data, this method can provide personal and regional risk levels, providing an alarm function which can be triggered by being close enough to a high-risk area or if the infected person's risk level is greater than the set threshold within the Bluetooth interconnection range. The system can provide a wealth of data on the location of infection sources and close contacts, offering valuable technical support for rapid and efficient epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus's spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review's insights into the pandemic response. Finally, we summarize the study's limitations and highlight promising future research directions in DCT. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial R t through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible–exposure–infection–quarantine–recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy. [ABSTRACT FROM AUTHOR]

Since its emergence, coronavirus disease 2019 (COVID-19) has affected the entire world and all commerce and industries, including healthcare systems. COVID-19 adversely affects cancer patients because they are immunocompromised. Increased COVID-19 infection and shortage of medical supplies, beds and healthcare workers in hospitals affect cancer care. This paper includes a description of the existing research that shows the impact of COVID-19 on the management of cancer patients. Aged people with various chronic conditions such as cancer and comorbidities face more challenges as they have a greater risk of disease severity. COVID-19 has affected care delivery, including patient management, and has been responsible for increased mortality among cancer patients. Cancer patients with severe symptoms require regular therapies and treatment; therefore, they have a higher risk of exposure. Due to the risk of transmission, various steps were taken to combat this disease; however, they have affected the existing operational efficiency. Herein, we present the changing priorities during COVID-19, which also affected cancer care, including delayed diagnosis, treatment, and surgeries. [ABSTRACT FROM AUTHOR]

The emergency of the pandemic and the absence of treatment have motivated researchers in all the fields to deal with the pandemic situation. In the field of computer science, major contributions include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Since the emergence of information technology, data science and machine learning have become the most widely used techniques to detect, diagnose, and predict the positive cases of COVID-19. This paper presents the prediction of confirmed cases of COVID-19 and its mortality rate and then a COVID-19 warning system is proposed based on the machine learning time series model. We have used the date and country-wise confirmed, detected, recovered, and death cases features for training of the model based on the COVID-19 dataset. Finally, we compared the performance of time series models on the current study dataset, and we observed that PROPHET and Auto-Regressive (AR) models predicted the COVID-19 positive cases with a low error rate. Moreover, death cases are positively correlated with the confirmed detected cases, mainly based on different regions' populations. The proposed forecasting system, driven by machine learning approaches, will help the health departments of underdeveloped countries to monitor the deaths and confirm detected cases of COVID-19. It will also help make futuristic decisions on testing and developing more health facilities, mostly to avoid spreading diseases. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is transmitted by contacting; however, the virus is so active that it can attach to objects and be transmitted from objects to humans via such contacting. The virus, which spreads through some living or inanimate-mediated processes, is more dangerous. On the basis of the routine contact transmission of infectious diseases, this paper further discusses the scope and efficiency of infectious diseases with indirect transmission. Through the study of two different transmission routes, the dynamic models of infectious diseases were constructed. The propagation of these two processes is theoretically studied using a differential equation model and stability analysis theory, and some actual virus propagation processes are simulated by numerical solutions. The prevention and control methods of infectious diseases are given, which lay the theoretical foundation for the discussion of related problems in practical application. [ABSTRACT FROM AUTHOR]

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy. [ABSTRACT FROM AUTHOR]

The date assumed as the beginning of the pandemic in Poland is 4 March 2020, the date of the first confirmed case of the virus. This article presents the actions undertaken by the management of underground hard coal mining plants concerning the risk of epidemic related to SARS-CoV-2. This work shows a set of implemented recommendations, guidelines and decisions, which were established after the appearance of the first wave of cases in Poland. What is more, it discusses measures aiming at reducing the risk of spreading the coronavirus among the mineworkers. The suggestions for different variants of the decision-making process concerning the pandemic and which have an enormous impact on the operating expenses of the company are also made. The paper presents the results of the study on actions taken in individual mining companies and good work practices recommended to be applied. [ABSTRACT FROM AUTHOR]

Drones have evolved significantly in recent years, acquiring greater autonomy and carrier capacity. Therefore, drones can play a substantial role in civil medicine, especially in emergency situations or for the detection and monitoring of disease spread, such as during the COVID-19 pandemic. The aim of this paper is to present the real possibilities of using drones in field rescue operations, as well as in nonsegregated airspace, in order to obtain solutions for monitoring activities and aerial work in support of the public health system in crisis situations. The particularity of our conceptual system is the use of a "swarm" of fast drones for aerial reconnaissance that operate in conjunction, thus optimizing both the search and identification time while also increasing the information area and the operability of the system. We also included a drone with an RF relay, which was connected to a hub drone. If needed, a carrier drone with medical supplies or portable devices can be integrated, which can also offer two-way audio and video communication capabilities. All of these are controlled from a mobile command center, in real time, connected also to the national dispatch center to shorten the travel time to the patient, provide support with basic but life-saving equipment, and offer the opportunity to access remote or difficult-to-reach places. In conclusion, the use of drones for medical purposes brings many advantages, such as quick help, shortened travel time to the patient, support with basic but life-saving equipment, and the opportunity to access remote or difficult-to-reach places. [ABSTRACT FROM AUTHOR]

Occupancy density is a dynamic measurement that reveals the relationship between the floor area and occupant count, usually in a room or building. The research presented in this paper probes further into the relationship between the physical properties of space and occupants' activity, to expand the understanding of occupancy density. The presented outcome is an evidence-based technique for determining room and activity-specific occupancy density limits that can support the design and be integrated into the design process. In this study, occupant information, namely, positioning, is simulated in the spatial context, including room dimensions and furniture layout. Controllable distancing variables, such as those globally introduced in response to the COVID-19 pandemic to prevent the spread of infectious diseases in indoor environments, are used to assess occupancy density thresholds. [ABSTRACT FROM AUTHOR]

As a result of the spread of the coronavirus (COVID-19), thousands of small companies around the world have been severely disrupted. Many business professionals, particularly entrepreneurs, suffer from the unprecedented magnitude of the lockdown of social activities, which is combined with limits on individual mobility. This study investigates the resilience of entrepreneurs—which is characterized by hardiness, resourcefulness, and optimism—as well as the relationship between resilience and creative performance. Additionally, the mediating role of institutional orientation is investigated in order to highlight how contextual factors influence this relationship. Using a quantitative study approach and structural equation modeling data analysis technique, 390 entrepreneurs were investigated, and the analyzed data demonstrate that entrepreneurs' ability to persevere in the face of adversity is strongly related to their ability to innovate, with institutional orientation serving as a partial mediating variable. Implications and future research opportunities are also explored in the paper. [ABSTRACT FROM AUTHOR]

Recently, the rapid transmission of Coronavirus 2019 (COVID-19) is causing a significant health crisis worldwide. The World Health Organization (WHO) issued several guidelines for protection against the spreading of COVID-19. According to the WHO, the most effective preventive measure against COVID-19 is wearing a mask in public and crowded areas. It is quite difficult to manually monitor and determine people with masks and no masks. In this paper, different deep learning architectures were used for better results evaluations. After extensive experimentation, we selected a custom model having the best performance to identify whether people wear a face mask or not and allowing an easy deployment on a small device such as a Jetson Nano. The experimental evaluation is performed on the custom dataset that is developed from the website (See data collection section) after applying different masks on those images. The proposed model in comparison with other methods produced higher accuracy (99% for training accuracy and 99% for validation accuracy). Moreover, the proposed method can be deployed on resource-constrained devices. [ABSTRACT FROM AUTHOR]

Featured Application: The work focuses on analyzing airplane boarding methods used in normal times and evaluating them by applying lessons learned during the COVID-19 pandemic. As a result, the airplane boarding methods used in practice by the airlines are analyzed through a series of health risks indicators and an operational indicator—the boarding time. The COVID-19 pandemic has produced changes in the entire aviation industry, including adjustments by airlines to keep the middle seats of airplanes empty to reduce the risk of disease spread. In this context, the scientific literature has introduced new metrics related to passengers' health when comparing airplane boarding methods in addition to the previous objective of minimizing boarding time. As the pandemic concludes and the aviation industry returns to the pre-pandemic situation, we leverage what we learned during the pandemic to reduce the health risk to passengers when they are not social distancing. In this paper, we examine the performance of classical airplane boarding methods in normal times but while considering the health metrics established during the pandemic and new metrics related to passenger health in the absence of social distancing. In addition to being helpful in normal times, the analysis may be particularly helpful in situations when people think everything is normal but an epidemic has begun prior to being acknowledged by the medical scientific community. The reverse pyramid boarding method provides favorable values for most health metrics in this context while also minimizing the time to complete boarding of the airplane. [ABSTRACT FROM AUTHOR]

This paper investigates the spatial dimension of socioeconomic and demographic factors behind COVID-19 vaccine hesitancy. With a focus on a county with considerable sociodemographic diversity in the state of Texas, USA, we apply regression models to census-tract-level data of the unvaccinated population. In addition to disparities in accessing the vaccination service, particularly for residents in rural areas, empirical results confirm under-vaccination among lower socioeconomic neighborhoods and communities with signs of distrust in government. The spatial model regressions further underscore the impact that vaccine hesitancy among residents in one community spread to its nearby communities. This observed spatial spillover effect is attributable to the geographic interactions of similar socioeconomic groups. [ABSTRACT FROM AUTHOR]

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area. [ABSTRACT FROM AUTHOR]

With the outbreak of the COVID-19 pandemic, the global tourism market has become one of the most affected sectors of the economy. In this research, the literature on the economic effects created by COVID-19 on a global level is first studied and the measures and restrictions that governments are obliged to take in order to suppress and prevent the spread of the coronavirus are analyzed. Next, there is an attempt to empirically estimate a system of equations regarding the two channels of influence of COVID-19 on tourism, making use of cross-sectional data, and specifically for a sample of 38 countries that launched vaccination up until the end of 2020. The explained results confirm that tourism is directly affected by the spread of COVID-19, due to the effort of travelers to avoid illness, but also due to the measures taken by governments to limit it. Tourism is also indirectly affected, due to the negative impact on income. Using 3SLS, an equation was developed to calculate the direct and indirect impact of COVID-19 on tourism. Notably, the dependent variable (tourism expenditure) exhibited a positive correlation with the independent variable (GDP) and a negative correlation with the total COVID-19 cases. Consequently, it was determined that a unit increase in the COVID-19 variable led to a reduction in international tourism expenditure by USD 859,237. Finally, there is a concern, and further investigation is needed, regarding the effect of vaccination against COVID-19 on tourism, which, while it is expected to be negative, is not confirmed by the results. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has made the world face a whole new reality, and the measures taken for slowing the spread of the disease included social distancing, as well as national, regional, or local lockdowns and restrictions regarding the movement of people. For such an important sector of the Romanian economy as the construction industry, the restrictive measures imposed by the authorities led to postponing, delaying, or stopping the current projects. This paper aims at investigating the main effects of the COVID-19 pandemic on the Romanian construction sector, as well as the role of the digitalization process in mitigating the negative impact on the companies activating in this domain, in the context of the current levels of use of information technologies. The research involved the use of qualitative (exploratory in-depth interviews) and quantitative (online surveys applied to members of the local building sector, such as engineers, architects, contractors, and managers) data collection methods, respectively descriptive statistics for data categorization and interpretation. In general, the Romanian construction sector was only slightly affected, with the most significant negative effects being the delays and cost overrun due to safety and health issues. This paper is one of the first studies to explore and assess the effects of COVID-19 on the Romanian construction industry and the role of digitalization in reducing the pandemic impact. The main findings reveal that the companies in this industry that have a high digitalization degree overpassed in a smoother manner the current pandemic situation. [ABSTRACT FROM AUTHOR]

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model's fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. [ABSTRACT FROM AUTHOR]

This study aimed to identify the individual, organizational, and environmental factors which contributed to COVID-19-related outcomes in long-term care facilities (LTCFs). A systematic review was conducted to summarize and synthesize empirical studies using a multi-level analysis approach to address the identified influential factors. Five databases were searched on 23 May 2023. To be included in the review, studies had to be published in peer-reviewed journals or as grey literature containing relevant statistical data. The Joanna Briggs Institute critical appraisal tool was employed to assess the methodological quality of each article included in this study. Of 2137 citations identified after exclusions, 99 records met the inclusion criteria. The predominant individual, organizational, and environmental factors that were most frequently found associated with the COVID-19 outbreak comprised older age, higher dependency level; lower staffing levels and lower star and subset domain ratings for the facility; and occupancy metrics and co-occurrences of outbreaks in counties and communities where the LTCFs were located, respectively. The primary individual, organizational, and environmental factors frequently linked to COVID-19-related deaths comprised age, and male sex; higher percentages of racial and ethnic minorities in LTCFs, as well as ownership types (including private, for-profit, and chain membership); and higher occupancy metrics and LTCF's size and bed capacity, respectively. Unfolding the risk factors collectively may mitigate the risk of outbreaks and pandemic-related mortality in LTCFs during future endemic and pandemics through developing and improving interventions that address those significant factors. [ABSTRACT FROM AUTHOR]

The recent global pandemic of coronavirus disease 2019 (COVID-19) has enormously promoted the development of diagnostic technology. To control the spread of pandemic diseases and achieve rapid screening of the population, ensuring that patients receive timely treatment, rapid diagnosis has become the top priority in the development of clinical technology. This review article aims to summarize the current rapid nucleic acid diagnostic technologies applied to pandemic disease diagnosis, from rapid extraction and rapid amplification to rapid detection. We also discuss future prospects in the development of rapid nucleic acid diagnostic technologies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities. [ABSTRACT FROM AUTHOR]

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, this fatal disease has been the leading cause of the death of more than 3.9 million people around the world. This tragedy taught us that we should be well-prepared to control the spread of such infectious diseases and prevent future hazards. As a consequence, this pandemic has drawn the attention of many researchers to the development of portable platforms with short hands-on and turnaround time suitable for batch production in urgent pandemic situations such as that of COVID-19. Two main groups of diagnostic assays have been reported for the detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) including nucleic acid-based and protein-based assays. The main focus of this paper is on the latter, which requires a shorter time duration, less skilled technicians, and faces lower contamination. Furthermore, this paper gives an overview of the complementary metal-oxide-semiconductor (CMOS) biosensors, which are potentially useful for implementing point-of-care (PoC) platforms based on such assays. CMOS technology, as a predominant technology for the fabrication of integrated circuits, is a promising candidate for the development of PoC devices by offering the advantages of reliability, accessibility, scalability, low power consumption, and distinct cost. [ABSTRACT FROM AUTHOR]

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients' conditions and identifying their severity levels. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) is a pandemic to which nursing students are particularly susceptible. This study aims to comprehensively examine nursing students' knowledge, attitudes, risk perceptions, preventive behaviors related to COVID-19, and nursing intentions toward patients with the virus. A questionnaire was administered to 149 nursing students from two universities. Data on the respondents' general characteristics, knowledge levels, attitudes, perceived risk, preventive behaviors toward COVID-19, and nursing intentions toward COVID-19 patients were collected. The collected data were statistically analyzed using SPSS software (version 26.0). This involved descriptive statistics, independent t-tests, one-way ANOVA, Pearson's correlation coefficient, and stepwise multiple regression analyses. The analyses of the factors affecting nursing students' nursing intentions for COVID-19 patients showed that the most predictive factor was perceived risk (β = −0.38, p < 0.001), followed by attitudes (β = 0.29, p < 0.001) and preventive behaviors (β = 0.17, p = 0.017), which explained 26% of the variance in nursing intentions. Lowering the perceived risk of infectious diseases and cultivating positive attitudes and preventive behaviors can increase nursing students' intentions toward COVID-19 patients. Finally, infection management education programs and research on interventions for nursing students are necessary to enhance the quality of nursing care provided to patients with novel infectious diseases. [ABSTRACT FROM AUTHOR]

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization. [ABSTRACT FROM AUTHOR]

The goal of this study is to analyze associations between COVID-19 transmission and meteorological indicators in cities of the Black Sea region of Turkey, located specifically in the dampest area, with excess rainfall and recurring fog. In particular, the working hypothesis is that the widespread transmission of new coronavirus SARS-CoV-2 (leading to the airborne disease COVID-19) in cities can be explained by specific weather conditions, namely high levels of air humidity. Statistical evidence here does not seem, in general, to support the hypothesis that the accelerated transmission of COVID-19 in the studied cities can be explained by high levels of humidity because different meteorological, environmental, demographic, and socioeconomic factors also plays a critical role in the disease transmission dynamics of the investigated region. The main implications of our findings here are that the demographic structure of the population, climate indicators, organization of the health system, and environmental factors (e.g., air pollution, etc.) should be considered through a systemic approach when designing effective national and regional pandemic plans directed to implement health policies for facing new variants of COVID-19 and/or new airborne diseases, in order to reduce their negative effects on health, social and economic systems. [ABSTRACT FROM AUTHOR]

Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in the context of lockdown as a response to the global Omicron outbreak. In this study, we extended the existing literature by relating the initial-stage Omicron pandemic conditions with more comprehensive measures of the built environment, including density, diversity, design, distance to transit, and destination accessibility. The variations from the confirmed clusters of COVID-19 and asymptomatic infected cases before, during, and after the lockdown throughout the Omicron outbreak were identified geographically using GIS methods in 218 township-level divisions across Shanghai during the lockdown period. We also compared the regression results of the ordinary least-squares regression, geographically weighted regression, and geographically and temporally weighted regression. Our results show that (1) among all the built environment variables, metro line length, walking accessibility, hotel and inn density, and population exhibited positive significance in influencing pandemic prevalence; (2) spatial and temporal variations were evident in the association between accessibility, mobility, density-related built environment variables, and COVID-19 transmission across three phases: pre-lockdown, during lockdown, and post-lockdown. This study highlights the importance of targeted public health interventions in densely populated areas with high demand for public transit. It emphasizes the significance of transportation network layout and walking accessibility in controlling the spread of infectious diseases in specific urban contexts. By considering these factors, policymakers and stakeholders can foster urban resilience and effectively mitigate the impact of outbreaks, aligning with the objectives of the 2030 UN Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease quickly spreads due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties and computer-aided drug design (in silico studies) for optimizing anti-SARS-CoV-2 hits/leads. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created a global emergency in many sectors. The spread of the disease can be subdued through timely vaccination. The COVID-19 vaccination process in various countries is ongoing and is slowing down due to multiple factors. Many studies on European countries and the USA have been conducted and have highlighted the public's concern that over-vaccination results in slowing the vaccination rate. Similarly, we analyzed a collection of data from the gulf countries' citizens' COVID-19 vaccine-related discourse shared on social media websites, mainly via Twitter. The people's feedback regarding different types of vaccines needs to be considered to increase the vaccination process. In this paper, the concerns of Gulf countries' people are highlighted to lessen the vaccine hesitancy. The proposed approach emphasizes the Gulf region-specific concerns related to COVID-19 vaccination accurately using machine learning (ML)-based methods. The collected data were filtered and tokenized to analyze the sentiments extracted using three different methods: Ratio, TextBlob, and VADER methods. The sentiment-scored data were classified into positive and negative tweeted data using a proposed LSTM method. Subsequently, to obtain more confidence in classification, the in-depth features from the proposed LSTM were extracted and given to four different ML classifiers. The ratio, TextBlob, and VADER sentiment scores were separately provided to LSTM and four machine learning classifiers. The VADER sentiment scores had the best classification results using fine-KNN and Ensemble boost with 94.01% classification accuracy. Given the improved accuracy, the proposed scheme is robust and confident in classifying and determining sentiments in Twitter discourse. [ABSTRACT FROM AUTHOR]

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing. [ABSTRACT FROM AUTHOR]

The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F 1 scores for the clusters and also better clustering quality metrics compared to baselines. [ABSTRACT FROM AUTHOR]

At the end of 2019, an outbreak of the novel coronavirus (COVID-19) made a profound impact on the country's production and people's daily lives. Up until now, COVID-19 has not been fully controlled all over the world. Based on the clinical research progress of infectious diseases, combined with epidemiological theories and possible disease control measures, this paper establishes a Susceptible Infected Recovered (SIR) model that meets the characteristics of the transmission of the new coronavirus, using the least square estimation (LSE) method to estimate the model parameters. The simulation results show that quarantine and containment measures as well as vaccine and drug development measures can control the spread of the epidemic effectively. As can be seen from the prediction results of the model, the simulation results of the epidemic development of the whole country and Nanjing are in agreement with the real situation of the epidemic, and the number of confirmed cases is close to the real value. At the same time, the model's prediction of the prevention effect and control measures have shed new light on epidemic prevention and control. [ABSTRACT FROM AUTHOR]

Due to the worldwide outbreak of COVID-19, many strategies and models have been put forward by researchers who intend to control the current situation with the given means. In particular, compartmental models are being used to model and analyze the COVID-19 dynamics of different considered populations as Susceptible, Exposed, Infected and Recovered compartments (SEIR). This study derives control-oriented compartmental models of the pandemic, together with constructive control laws based on the Lyapunov theory. The paper presents the derivation of new vaccination and quarantining strategies, found using compartmental models and design methods from the field of Lyapunov theory. The Lyapunov theory offers the possibility to track desired trajectories, guaranteeing the stability of the controlled system. Computer simulations aid to demonstrate the efficacy of the results. Stabilizing control laws are obtained and analyzed for multiple variants of the model. The stability, constructivity, and feasibility are proven for each Lyapunov-like function. Obtaining the proof of practical stability for the controlled system, several interesting system properties such as herd immunity are shown. On the basis of a generalized SEIR model and an extended variant with additional Protected and Quarantined compartments, control strategies are conceived by using two fundamental system inputs, vaccination and quarantine, whose influence on the system is a crucial part of the model. Simulation results prove that Lyapunov-based approaches yield effective control of the disease transmission. [ABSTRACT FROM AUTHOR]

The built environment is the global sector with the greatest energy use and greenhouse gas emissions. As a result, building energy savings can make a major contribution to tackling the current energy and climate change crises. Fluid dynamics models have long supported the understanding and optimization of building energy systems and have been responsible for many important technological breakthroughs. As Covid-19 is continuing to spread around the world, fluid dynamics models are proving to be more essential than ever for exploring airborne transmission of the coronavirus indoors in order to develop energy-efficient and healthy ventilation actions against Covid-19 risks. The purpose of this paper is to review the most important and influential fluid dynamics models that have contributed to improving building energy efficiency. A detailed, yet understandable description of each model's background, physical setup, and equations is provided. The main ingredients, theoretical interpretations, assumptions, application ranges, and robustness of the models are discussed. Models are reviewed with comprehensive, although not exhaustive, publications in the literature. The review concludes by outlining open questions and future perspectives of simulation models in building energy research. [ABSTRACT FROM AUTHOR]

The worldwide spread of coronavirus COVID-19 infections demonstrates the great need for personal protective equipment and, in particular, hygiene masks. These masks are essential for the primary protection of the respiratory tract against pathogens such as viruses and bacteria that are infectious and transmitted through the air as large droplets or via small airborne particles. The use of protective masks will continue to accompany humans for an indefinite period of time, and therefore there is an urgent need for a safe method to extend their usability by reusing them under perspective with minimal loss of protective properties. Nanofiber mats are widely used in masks and in this study the reusability of nanofiber mats is investigated by washing them at different temperatures. This paper shows the first measurements of the washability of nanofiber mats. Furthermore, the air permeability is measured, and the evaporation resistance is evaluated. According to the results of this study, the air permeability performance of nanofiber mats does not change significantly after washing, confirming the possibility of reuse. [ABSTRACT FROM AUTHOR]

Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This study proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs method captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. Specifically, we modelled the system of ODEs as one network and the time-varying parameters as another network to address significant unknown parameters and limited data. Such structure of the PINNs method is in line with the prior epidemiological correlations and comprises the mismatch between available data and network output and the residual of ODEs. The experimental findings on real-world reported data data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs method can be successfully extended to other regions and infectious diseases. [ABSTRACT FROM AUTHOR]

The modeling of pandemics is significant in understanding and addressing the spread of infectious diseases. This study introduces a novel and highly flexible extension of the asymmetric unit Burr–Hatke distribution, termed the power Burr–Hatke distribution (PUBHD), and comprehensively investigates its mathematical properties. Multiple parameter estimation methods are employed, and their asymptotic behavior is analyzed through simulation experiments. The different estimation techniques are compared to identify the most efficient approach for estimating the distribution's parameters. To demonstrate the applicability and usefulness of the PUBHD model, we conducted a case study using a sample from the COVID-19 dataset and compared its performance with other established models. Our findings show that the PUBHD model provides a superior fit to the COVID-19 dataset and offers a valuable tool for accurately modeling real-life pandemics. [ABSTRACT FROM AUTHOR]