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The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform. [ABSTRACT FROM AUTHOR]

In this paper, the epidemic spreading was investigated from the point of view of fractal. Firstly, the real network was abstracted as a fully connected fractal network. Based on the fractal network, the fractal spreading process was studied. The fractal spreading process was simulated to analyze the change of infection density during transmission. The results showed that the infection density presented an upward trend of the ladder-shaped, and a jumping change in infection density occurred during a certain time. As an illustration, the pandemic of COVID-19 was analyzed, the results indicated that the proposed method was valid. Experiments and analyses have shown that intervention at key jump points in virus propagation can effectively control the spread of the virus. [ABSTRACT FROM AUTHOR]

"COVID-19" is one of the most important issues facing our daily lives. Thousands of former infected people find it difficult to regain their health before infection with Corona, Today, they suffer from fatigue, pain, and constant apnea. This is one of the threats to our world today. This negatively affects many of the political and economic repercussions. Artificial Intelligence (AI) technology can build a smart platform to automatically monitor and predict the spread of this virus. AI provides up-to-date information useful in predicting potential sites of infection and preventing this disease, this is by utilizing data mining and realtime processing. The use of the Bayesian network model is a useful approach to monitoring and diagnosing (COVID-19) in different age groups. This paper presents Bayesian network techniques for (COVID-19) testing to identify the most affected age groups (COVID-19) f rom the database. The results show that an AI approach that includes data analysis, mining, and processing is a powerful technology. The decision-maker can quickly detect and prevent the disease, identify the age groups most affected by the disease, prevent infection and fight this epidemic. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to understand the factors that contribute to the number of reported coronavirus (COVID-19) deaths among low-income and high-income countries, and to understand the sources of differences between these two groups of countries. Design/methodology/approach: Multiple linear regression models evaluate the socio-economic factors that determine COVID-19 deaths in the two groups of countries. The Oaxaca–Blinder decomposition is used to examine sources of differences between these two groups. Findings: Low-income countries report a significantly lower average number of COVID-19 deaths compared to high-income countries. Community mobility and the easiness of carrying the virus from one place to another are significant factors affecting the number of deaths, while life expectancy is only significant in high-income countries. Higher health expenditure is associated with more reported deaths in both high- and low-income countries. Factors such as the transport infrastructure system, life expectancy and the percent of expenditure on health lead to the differences in the number of deaths between high- and low-income countries. Social implications: Our study shows that mobility measures taken by individuals to limit the spread of the virus are important to prevent deaths in both high- and low-income countries. Additionally, our results suggest that countries with weak health institutions underestimate the number of deaths from COVID-19, especially low-income countries. The underestimation of COVID-19 deaths could be affecting a great number of people in poverty in low-income economies. Originality/value: This paper contributes to the emerging literature on COVID-19 and its relation to socio-economic factors by examining the differences in reported between deaths between rates in low-income and high-income countries. [ABSTRACT FROM AUTHOR]

The health emergency linked to Covid-19 brought to the fore the problem of the usefulness and correct application of the precautionary principle. In the paper proposed for the call, the topic will be analyzed starting from the foundations of the precautionary principle, to see consequently how it must be 'handled' in practice when the Administration (in the Italian fight against the pandemic, the Government, but also the Regions) is called to decide in contexts of health crisis. Particular attention will be paid, just from the beginning, to the relationship between science and Public Administration, in order to demonstrate how the precautionary approach represents a 'rule of action' for the public decision-maker when there is no full scientific certainty. In this perspective, the analysis will be developed starting from the definition of a general context: that one represented by the so-called 'irreducible uncertainty.' This premise will be the starting point to define a law of uncertain science,' which 'follows' the facts and is characterized for its flexibility. The problem will then be reported to the administrative decisions, called in emergency times to be 'adaptive' and reviewable. The reflection on precaution 'in action' will have the Italian case as an observation 'laboratory.' In this perspective, the investigation will be conducted by looking at the 'answer' of the Italian legal system to the emergency related to Covid-19. This will lead to see if the precautionary approach has been taken seriously by the Italian Administration and, subsequently, what characters have taken the measures to fight against the spread of the Coronavirus outbreak. In conclusion, it will be necessary to understand whether the 'postulates' of precaution 'in the books' have been translated into an adequate precaution 'in action.' In other terms, the attention will be focused on two different aspects: the first related to the 'time' of the action; the second to the content of the measures taken to fight against the spread of the virus. This will allow us to understand if the Italian Government acted promptly (in compliance with the precautionary approach) and what was the decision-making process that brought to these measures. [ABSTRACT FROM AUTHOR]

Across the globe Covid-19 has created economic hardship for businesses as well as communities and affected the livelihood. Intensive of spread of the corona virus has forced the Indian Government to announce lockdown on March 24, 2020.Indian economy was already struggling with lower growth in Gross Domestic Product (GDP), theCOVID-19 pandemic smashed the Indian industries in an unprecedented and unexpected way. It is the first time in the history that COVID-19 pandemic has influenced industrial demand, supply and availability of workforce in the same time globally. Month's together lockdown and global slowdown has considerably disrupted demand-supply chain and dented the economy as well as industries deeply. Present lockdown has impacted industrial sector enormously with lessened industrial output and lower profitability. The main objective of this paper is to study and analyze the impact of covid 19 on industrial sector of India and to know how industries are striving to cope up with lockdown and its impact. Genuine effort have made in this paper to analyze the current Industrial sectors situation in India. [ABSTRACT FROM AUTHOR]

aim Gozum,1 Harvey Gain Capulong,1 Jose Ma Gopez,1 Joseph Renus Galang2,31Center for Christian Formation and Praxis, Angeles University Foundation, Angeles City, 2009, Philippines; 2Graduate School, Angeles University Foundation, Angeles City, 2009, Philippines; 3Theology and Religious Education Department, De La Salle University, Manila, 0922, PhilippinesCorrespondence: Ivan Efreaim Gozum Email [email protected] Background: This research investigates different approaches by cultural, religious and political groups in ensuring public health during the COVID-19 pandemic. During the pandemic, several methods were done by governments and healthcare institutions to mitigate the spread of the virus. Methods: This study uses content analysis, specifically a literature review, on existing online literature found in online journals, books, and reports relevant to the COVID-19 pandemic. The paper presents the available qualitative data including substantive findings as well as theoretical and methodological foundations relevant to the topic. Results: Culture, religion and the state have roles to play in ensuring public health during the COVID-19 pandemic. These three areas can help in mitigating the transmission of the virus, managing patients, and aiding the rollout of COVID-19 vaccines. Conclusion: This paper ends by suggesting that, in ensuring public health during the COVID-19 pandemic, a multidisciplinary approach, involving a concerted effort among culture, religion and the state, is necessary. This approach is also recommended for any public health crisis in the future. [ABSTRACT FROM AUTHOR]

The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 –Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale. [ABSTRACT FROM AUTHOR]

These days, one of the biggest challenges in the world is dealing with the outbreak of the Co vid 19 virus. Recently, new variants of this virus have been identified that have a mucli liigher rate of transmission. To effectively control and manage the spread of the disease, a clear understanding of its transmission dynamics and effective control techniques to reduce or inllibit the spread of the virus is necessary. Although vaccine production and distribution are currently underway. Non-Pharmacological Interventions (NPI) continue to be an important and fundamental strategy for controlling the spread of the virus in various countries around the world. In this paper. Covid 19 dynamics is modeled using four well-known categories (SEIR): Susceptible-Exposed-Infected-Recovered. Since the parameters of the model have uncertainty, a robust control method should be designed. In this paper, using fractional calculus and fuzzy logic, a robust fuzzy fractional-order sliding mode controller (FOFSMC) for Covid 19 dynamics is proposed, which aims to control the prevalence of the disease using NPI. The proposed method is implemented both on the integer and fractional order model. Finally, the performance of the proposed controller on the new variant of the Covid 19 virus with a faster disease transmission rate will be evaluated. [ABSTRACT FROM AUTHOR]

This paper is concerned with a reaction–diffusion system on a growing domain describing the spatial spreading of bacterial and viral diseases induced by fecal–oral transmission. We introduce a threshold parameter Θ 0 ρ by means of the eigenvalue problem to explore the stability of the disease-free and endemic equilibria. By overcoming the difficulty induced by the time-dependent diffusion rates, we are further able to investigate the asymptotic behavior of solutions to the reaction–diffusion system. Compared to the model counterpart with fixed domain, biological impacts of a growing domain on the spreading of infectious diseases are obtained. We conclude that the growth of the domain is detrimental to the prevention and control of infectious diseases. [ABSTRACT FROM AUTHOR]

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is one of the rarest events of global crises where a viral pathogen infiltrates every part of the world, leaving every country face an inevitable threat of having to lock down major cities and economic hubs and put firm restrictions on citizens thus slowing down the economy. The risk of removal of lockdowns is the emergence of new waves of a pandemic causing a surge in new cases. These facts necessitate the containment of the virus when the lockdowns end. Wearing masks in crowded places can help restrict the spread of the virus through minuscule droplets in the air. Through the automatic detection, enumeration, and localisation of masks from closed-circuit television footage, it is possible to keep violations of post-COVID regulations in check. In this paper, we leverage the Single-Shot Detection (SSD) framework through different base convolutional neural networks (CNNs) namely VGG16, VGG19, ResNet50, DenseNet121, MobileNetV2, and Xception to compare performance metrics attained by the different variations of the SSD and determine the efficacies for the best base network model for automatic mask detection in a post COVID world. We find that Xception performs best among all the other models in terms of mean average precision. [ABSTRACT FROM AUTHOR]

Ventilation systems are one of the most effective strategies to reduce the risk of viral infection transmission in buildings. However, insufficient ventilation rates in crowded spaces, such as schools, would lead to high risks of infection transmission. On the other hand, excessive ventilation rates might significantly increase cooling energy consumption. Therefore, energy-efficient control methods, such as Demand Control Ventilation systems (DCV), are typically considered to maintain acceptable indoor air quality. However, it is unclear if the DCV-based controls can supply adequate ventilation rates to minimize the probability of infection (POI) in indoor spaces. This paper investigates the benefits of optimized ventilation strategies, including conventional mechanical systems (MV) and DCV, in reducing the POI and cooling energy consumption through a detailed sensitivity analysis. The study also evaluates the impact of the ventilation rate, social distancing, and number of infectors on the performance of the ventilation systems. A coupling approach of a calibrated energy model of a school building in Jeddah, KSA, with a validated Wells–Riley model is implemented. Based on the findings of this study, proper adjustment of the DCV set point is necessary to supply adequate ventilation rates and reduce POI levels. Moreover, optimal values of 2 ACH for ventilation rate and 2 m for social distance are recommended to deliver acceptable POI levels, cooling energy use, and indoor CO2 concentration for the school building. Finally, this study confirms that increasing the ventilation rate is more effective than increasing social distancing in reducing the POI levels. However, this POI reduction is achieved at the cost of a higher increase in the cooling energy. [ABSTRACT FROM AUTHOR]

Tourism is a strategic sector for creating wealth, employment, and foreign exchange. However, it is, also, vulnerable to external factors such as natural disasters, socio-political and economic crises, and so on. The COVID-19 pandemic has had a tragic impact on the tourism sector globally, leading to new regulations and restrictions to stop the spread of the virus. These restrictions changed the rules of the game and thus influenced tourism supply and demand. This paper explores the impact of the sanitary and regulatory restrictions on tourism industry in Morocco, using a quantitative exploratory methodology based on secondary data and a questionnaire addressed to private touristic actors. The main findings show that the closure of borders, confinement and constraints on mobility are the main regulatory restrictions that have had the greatest impact on tourism in Morocco, followed by closures & curfews, which have had a substantial impact on the tourism sector. Social distancing has a high-tomedium impact on tourism activity, and finally, the protection & hygiene measures have a low impact on the performance of the tourism establishments. [ABSTRACT FROM AUTHOR]

The pandemic spreading of the COVID-19 virus has led to the global need to introduce, often by law, the medical face mask, which can undoubtedly be considered as "the object of 2020." In a few months, most human faces around the world in the public space, but also often in the private space, have been covered with various kinds of protective masks. Very soon, these objects have become the centre of several discursive productions, going from medical reports to media coverage, from artistic representations to ironic memes. The medical face mask was not totally new in the west, where it was already present in special circumstances, like dentists' studios or emergency rooms, and was quite familiar in the east, especially in Japan, China, and Korea. Yet such massive introduction changed the meaning of the medical face mask in every context. Old habits were reconfigured or clashed with the new ones, giving rise to a novel syntax, semantics, and pragmatics of the human face in conjunction with this device and in the context of the global pandemic. The present paper offers an introduction to a semiotic mapping of such radical cultural change and its likely consequences. [ABSTRACT FROM AUTHOR]

The health organizations around the world are currently facing one of the greatest challenges, to overcome the current global pandemic, COVID-19. It erupted in December 2019, in Wuhan City, China. It spreads rapidly throughout the world within couple of months. In this paper, the data of the COVID-19 have been collected, organized, analyzed and interpreted using the discrete-time model of SIR epidemic model. Moreover, results for several countries from different regions of the world have been obtained. Furthermore, comparative study has been carried out for the countries under consideration. The comparison was performed for the data of different countries on same dates of each month. However, the calculations are carried out for thirteen consecutive weeks, to investigate the rate of spread and the control of the disease in these countries. This guides us to some important concepts like factors favoring the spread of virus and those resisting the spread. Different regions are studied and their data have been evaluated to know which regions are the most effected. This study helps to know the important factors about the behavior of the coronavirus in different environments, such as lockdowns, temperatures, humidity and other restrictions. The proposed concepts and equations can be used to project the upcoming behavior of the pandemic. [ABSTRACT FROM AUTHOR]

The analysis of global epidemics, such as SARS, MERS, and COVID-19, suggests a hierarchical structure of the epidemic process. The pandemic wave starts locally and accelerates through human-to-human interactions, eventually spreading globally after achieving an efficient and sustained transmission. In this paper, we propose a hierarchical model for the virus spread that divides the spreading process into three levels: a city, a region, and a country. We define the virus spread at each level using a modified susceptible–exposed–infected–recovery–dead (SEIRD) model, which assumes migration between levels. Our proposed controlled hierarchical epidemic model incorporates quarantine and vaccination as complementary optimal control strategies. We analyze the balance between the cost of the active virus spread and the implementation of appropriate quarantine measures. Furthermore, we differentiate the levels of the hierarchy by their contribution to the cost of controlling the epidemic. Finally, we present a series of numerical experiments to support the theoretical results obtained. [ABSTRACT FROM AUTHOR]

Background: Over the past decades, several viral diseases transmitted by Aedes mosquitoes—dengue, chikungunya, Zika—have spread outside of tropical areas. To limit the transmission of these viruses and preserve human health, the use of mosquito traps has been developed as a complement or alternative to other vector control techniques. The objective of this work was to perform a systematic review of the existing scientific literature to assess the efficacy of interventions based on adult mosquito trap to control Aedes population densities and the diseases they transmit worldwide. Methods and findings: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted using the PubMed and Scopus databases. Among the 19 selected papers, lethal ovitraps were used in 16 studies, host-seeking female traps in 3 studies. Furthermore, 16 studies focused on the control of Ae. aegypti. Our review showed great heterogeneity in the indicators used to assess trap efficacy: e.g., the number of host-seeking females, the number of gravid females, the proportion of positive containers, the viral infection rate in female mosquitoes or serological studies in residents. Regardless of the type of studied traps, the results of various studies support the efficacy of mass trapping in combination with classical integrated vector control in reducing Aedes density. More studies with standardized methodology, and indicators are urgently needed to provide more accurate estimates of their efficacy. Conclusions: This review highlights gaps in the demonstration of the efficacy of mass trapping of mosquitoes in reducing viral transmission and disease. Thus, further large-scale cluster randomized controlled trials conducted in endemic areas and including epidemiological outcomes are needed to establish scientific evidence for the reduction of viral transmission risk by mass trapping targeting gravid and/or host-seeking female mosquitoes. Author summary: Over the past decades, several viral diseases transmitted by Aedes mosquitoes—dengue, chikungunya, Zika—have spread outside of tropical areas. To limit the transmission of these viruses and preserve environment and human health, the use of mosquito traps has been developed as a complement or alternative to other vector control techniques. This review support the efficacy of mass trapping in combination with classical integrated vector control on the reduction in mosquito vector populations within several weeks of deployment. However, this research highlights gaps in the demonstration of the efficacy of mass trapping of mosquitoes in reducing viral transmission and disease. Thus, further studies are needed to establish scientific evidence for the reduction of viral transmission risk by mass trapping targeting gravid and/or host-seeking female mosquitoes. [ABSTRACT FROM AUTHOR]

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated. The experimental results show that taking into account the cold chain input and re-positive can effectively simulate the spread of the epidemic. The research results have important research value and practical significance for the prevention and control of the COVID-19 and the prediction of important time nodes. [ABSTRACT FROM AUTHOR]

SMEs constitute the backbone of the Syrian economy and have suffered manifold challenges due to the continuous Syrian war. COVID-19 added further pressures on Syrian SMEs and forced them to take certain adaptation strategies to survive. This paper aims to investigate the main challenges that face Syrian SMEs during the pandemic and illustrate how they respond to adversities that emerged from governmental intervention to control the spread of the virus. It also discusses the measures initiated by the government to support SMEs during the pandemic. Through interviewing persons from the Syrian SMEs' ecosystem, we find that high interest rates on SMEs' loans decline on demand as well as high inflation represent the main challenges. SMEs respond to these challenges by marketing products online, stock procurement, and strengthening connections with stakeholders. We recommend the Syrian authorities reduce lending rates and increase loan sizes available to SMEs to help them overcome the pandemic adversities. Innovative sources of funding, such as venture capital and equity partnerships, could reduce the funding costs of SMEs. Moreover, SMEs will immensely benefit from training in digital tools to enhance their expansion and survival opportunities. Furthermore, bazaars should be organized during the year to give SMEs the opportunity to gain continuous access to markets. In addition, incubation services should be revised, particularly to SMEs with great potential to grow, to create the suitable environment for them to scale and flourish. [ABSTRACT FROM AUTHOR]

Due to the coronavirus pandemic, viruses have suddenly become a prominent topic of discussion. As such, it is important to understand the life cycle and, ultimately, the process of inactivating viruses. To inactivate them it is important to block the mechanisms that they use to induce infection. Due to their ability to survive on various surfaces, appropriate cleaning practices to prevent viral spread and contamination of medical devices, practice equipment and surfaces are paramount. Depending on the type of virus and genome configurations, various mechanisms are used to induce a viral infection. Understanding the susceptibility of the viral outer coating is crucial to preventing viral spread, whether through droplet transmission or while on surfaces (fomite transmission). Knowledge of how viral receptors and proteins interact to facilitate the recognition, attachment and entry of a virus into the host cell is vital for an understanding of how disinfectants and antivirals can be effective. Viral replication, transcription and translation are also major targets of antivirals. This paper will review the physiology of two major ophthalmic viruses: adenovirus and herpes simplex. [ABSTRACT FROM AUTHOR]

The Covid 19 pandemic is a global, not only, health crisis with devasting impact on the world economy in general. These impacts are also visible more and more in food and agriculture sector as the main sector of population nourishment. In spite of adopted measures in many countries, spread of the virus is starting to distrupt the supply of agrofood products to markets and consumers, both within and across borders. Most major economies are expected to enter recession as a result of the Covid 19 pandemic, and the OECD has estimated that for each month the necessary containment measures continue the drop in output is equivalent to a decline in annual GDP growth up to 2 percentage points. How damaging these impacts tourn out to be for individual agricultural production of coutries and agricultural cooperatives which are consider as the more viable during crisis is the objective of this paper. [ABSTRACT FROM AUTHOR]

There is an ongoing debate regarding the economic consequences of public policies designed to curb public health crises, such as the COVID-19 pandemic. Many opponents of such policies claim that their economic costs may outweigh their health benefits. In this paper, we use synthetic control analysis to determine the impact of stay-at-home orders on weekly new jobless claims during the initial phase of the COVID-19 pandemic. Our analysis reveals that while new jobless claims spike following the stay-at-home orders, similar spikes are observed within our synthetic control. Specifically, we find that stay-at-home orders account for only 32 percent of the increase in new jobless claims, with the majority of the increase being driven by factors outside of the policy, such as the general spread of the virus and waning consumer confidence. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic revealed that many countries have failed to provide the general population with the cognitive tools to thoroughly understand and cope with a global health crisis. While scientists and leaders worldwide have struggled to discover ways to contain the spread of the virus, this difficult task has become overwhelming due to the limited ability of many citizens to grasp the urgency of the situation. Although in today's digitized world we have endless access to data and more ways to represent information and statistics than ever before, numerous incidents have demonstrated that the frequent misapprehension of data can cause confusion rather than clarity. This opinion paper examines how issues such as the misunderstanding of large quantities, fractions, probabilities, and mathematical modeling may be affecting the way people view the current pandemic. Finally, we also discuss how numeracy can act as a protective factor against motivated reasoning, which often affects how we consume information related to the pandemic. [ABSTRACT FROM AUTHOR]

The persistence and infectivity of SARS-CoV-2 in different postmortem COVID-19 specimens remain unclear despite numerous published studies. This information is essential to improve corpses management related to clinical biosafety and viral transmission in medical staff and the public community. We aim to understand SARS-CoV-2 persistence and infectivity in COVID-19 corpses. We conducted a systematic review according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocols. A systematic literature search was performed in PubMed, Science Direct Scopus, and Google Scholar databases using specific keywords. We critically reviewed the collected studies and selected the articles that met the criteria. We included 33 scientific papers that involved 491 COVID-19 corpses. The persistence rate and maximum postmortem interval (PMI) range of the SARS-CoV-2 findings were reported in the lungs (138/155, 89.0%; 4 months), followed by the vitreous humor (7/37, 18.9%; 3 months), nasopharynx/oropharynx (156/248, 62.9%; 41 days), abdominal organs (67/110, 60.9%; 17 days), skin (14/24, 58.3%; 17 days), brain (14/31, 45.2%; 17 days), bone marrow (2/2, 100%; 12 days), heart (31/69, 44.9%; 6 days), muscle tissues (9/83, 10.8%; 6 days), trachea (9/20, 45.0%; 5 days), and perioral tissues (21/24, 87.5%; 3.5 days). SARS-CoV-2 infectivity rates in viral culture studies were detected in the lungs (9/15, 60%), trachea (2/4, 50%), oropharynx (1/4, 25%), and perioral (1/4, 25%) at a maximum PMI range of 17 days. The SARS-CoV-2 persists in the human body months after death and should be infectious for weeks. This data should be helpful for postmortem COVID-19 management and viral transmission preventive strategy. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic erupted, attempts to contain the spread of the virus took two concurrent forms, including mobility restrictions (aka lockdowns) and the race to produce a vaccine. However, it is quite striking that, amidst both the lockdown and the race to produce a vaccine, the question of how COVID-19 survivors/patients coped with the disease has not received the degree of attention it deserved. To navigate this issue, we employed a sample consisting of 100 COVID-19 survivors; this paper explores the relationship between the biopsychosocial (BPS) impacts of COVID-19, death anxiety, and coping strategies. In this context, the mediating role of death anxiety is placed in the spotlight. The analysis reveals a significant positive association between the BPS impact of COVID-19 and death anxiety and a significant negative association between death anxiety and coping strategies among COVID-19 survivors. Thus, death anxiety mediates the relationship between the BPS impact and the coping strategies that COVID-19 survivors adopt. Given the general recognition of the validity of the BPS model in contemporary medical science and practice, a thorough examination of COVID-19 survivors and their experiences related to surviving is necessary to match the challenges of today, including the increased probability of pandemics. [ABSTRACT FROM AUTHOR]

This paper presents a position statement on combining computational fluid dynamics (CFD) and ergonomics to guide the design of personal protective equipment (PPE). We used CFD to simulate 36 exposure scenarios of an infected patient sneezing at different distances and different angles while facing either the front or the side of a healthcare worker with or without goggles. The results show that medical goggles indeed block most droplets from the outer surface, but many droplets still deposit on the bottom edge (especially at the nose), inside the air holes and on the side edge. However, the edges of medical goggles have fitment problems with people in different regions, and the air holes do not function as filters and cannot prevent fine droplets from entering the interior and contacting the eyes. Our research demonstrates the feasibility of studying the design of PPE for airtightness and protection by means of CFD. Practitioner summary: Computational fluid dynamics can quickly and efficiently reflect the airtightness design problems of PPE. A model was developed using CFD to examine the protective effect of medical goggles in preventing the airborne transmission of viruses. The model demonstrates the feasibility of using CFD to solve ergonomic problems. Abbreviations: CFD: computational fluid dynamics; PPE: personal protective equipment; WHO: the World Health Organisation; COVID-19: coronavirus disease 2019; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; OSHA: the Occupational Safety and Health Administration; CDC: the Centres for Disease Control; FEM: finite element method; 3M: Minnesota Mining and Manufacturing Corporation; SPH: smoothed particle hydrodynamics; AROM: active range of motion; DPM: discrete phase model; PISO: pressure implicit with splitting of operators; VR: virtual reality; AR: augmented reality [ABSTRACT FROM AUTHOR]

This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections. [ABSTRACT FROM AUTHOR]

A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications. [ABSTRACT FROM AUTHOR]

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals. [ABSTRACT FROM AUTHOR]

Research Question: The authors investigate the impact of Flexible Work Arrangements (FWAs) on employee behavior during the COVID-19 pandemic in the Republic of Serbia. The main question is whether FWA and in which manner influences employees' behavior, especially turnover intentions and employees' engagement. Motivation: Based on the study results of Azar et al. (2018), Masuda et al. (2011), and Laurel et al. (2009), the author planned to identify the relations between FWA and the behavior of employees in terms of Engagement and Turnover intention in the Covid - 19 pandemic era. Idea: The research is created by considering the changes in the way of operating in almost all business activities caused by this pandemic. Having in mind the pandemic, and spreading of the virus after that, there is a gradual shift of work from the company's office to FWAs, especially "work from home" and teleworking. Data: The research was conducted during 2020/21, on a sample of 219 employees on whom this pandemic had a great impact and brought changes in the way of working, which became quite different from that before. Data collection is performed via an electronic survey Google-Forms, where respondents were able to answer the questions asked in the questionnaire via their electronic devices at any time. Tools: To analyze the data collected by completing the questionnaire, the author used Smart PLS 3 software to make the data analysis relevant. During the analysis of data in Smart PLS, an independent variable was formed that refers to "Flexible Work Arrangements", while the dependent variables are "Engagement" and "Turnover intentions". Findings: Based on the conducted research, we conclude that "FWAs" have a positive impact on the Engagements of employees in the Republic of Serbia and a negative impact on Turnover intentions. All hypotheses are confirmed. Contribution: The paper contributes to the literature by empirically testing how certain factors affect the behavior of employees in the Republic of Serbia during the COVID - 19 pandemic. [ABSTRACT FROM AUTHOR]

Malaysia has been relatively successful in managing the COVID-19 pandemic, with the number of deaths and infections lower than neighboring countries and many developed economies. This paper will share Malaysia's experience in fighting the pandemic, particularly the key success factors in managing the health impact during the period of January to August 2020. The speedy preparation and planning by the Health Ministry even before the country registered its first case was instrumental in ensuring that the country was ready to face the pandemic. Lessons learned from previous experience with epidemics such as Nipah, SARS, MERS, and H1N1 were also key to the speedy responses. Effective communication helped to ensure the public's support of measures imposed by the government to reduce the spread of the virus. However, while the country managed the health crisis relatively well, the handling of the economy is rather poor, with the economic impact being much worse than what was experienced during the 1997–98 Asian financial crisis, and the 2008–09 global financial crisis. This paper will end with suggestions of several policy interventions to mitigate the economic impact of COVID-19, particularly for vulnerable groups. [ABSTRACT FROM AUTHOR]

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It is well known that SARS--CoV--2, can be transmitted through airborne diffusion of saliva droplets which travels into atmospheric air through a thermo-fluid dynamic interaction with it. In order to limit SARS-CoV-2 spread, social distancing is crucial. However, in this context is really important to emphasize that available knowledge is largely inadequate to make predictions on the airborne spreading of infectious droplets emitted during a cough and/or sneezing. The main aim of our research activity is to provide a contribution to thermo-fluid dyamic modeling of saliva droplets diffusion produced by coughing. In particular in this paper, several efforts were devoted to the analysis of impact of saliva chemical composition on the their spreading in space. [ABSTRACT FROM AUTHOR]

Containing the spread of a virus during a pandemic requires citizens to self-discipline and adopt precautionary measures. This paper focuses on one such measure: social distancing. Governments can force citizens to comply with social distancing by imposing mandates and increasing penalties. However, constitutional restraints prevent governments in democratic societies from utilizing extreme measures. Thus, a pandemic presents an extreme case in which the goals of security (virus containment) and individual freedom appear irreconcilable. Moreover, a pandemic presents collective action problems, because a few defectors, who can remain undetected, can impose incalculable costs on a society. This predicament leads many to make a case for draconian measures to force compliance. We present an alternative take that views social distancing as a coproduction process; that is, virus containment requires active participation and a high degree of cooperation from citizens. Because external costs are difficult to measure and it is near impossible to monitor and sanction violations, coercive measures that do not account for coproduction processes are unlikely to succeed. Instead, strengthening existing mechanisms for mutual monitoring and sanctioning that are consistent with the norms and values of the populace may yield more favorable outcomes. [ABSTRACT FROM AUTHOR]

This paper aims to initiate reflections on what an antifragile (Taleb, Anti-fragile. Things that gain from disorder. Penguin Books, London 2012) built environment might look like by furthering the debate on dynamic non-equilibrium resilience, specifically in terms of scale, urban morphology and social life in urban areas. It will do so by presenting a critical review of relevant literature on resilience in the built environment and linking it to what we know so far about the physical (i.e. geographical, morphological and so on) and socio-cultural conditions that have likely limited the spread of COVID-19 while maintaining quality in urban space in early 2020. As the current pandemic is sharpening our understanding of both the link between local and global action and the power encompassed in the exercise of professional and technical knowledge and practice, the paper concludes with (i) speculations on how the current crisis and its management (i.e. lockdown and social distancing measures in public space' use) might lead to radical changes to the way we think of, and design the conditions for, urban public life and sociability; and with (ii) an agenda for further research on what role urban forms and uses play in speeding or slowing viral spread in different contexts. [ABSTRACT FROM AUTHOR]

Starting from the first part of the previous year, the mankind faced a very unusual challenge. Starting with the spread of an animal-based virus found in the exotic food markets in Wuhan Province, China, the SARS-COV-2 virus quickly began to spread on a global basis on the entire planet. With such a high contagiousness it was only a matter of time till the virus made its presence in the countries all over the world, infecting many people and, unfortunately, rising the death toll every day. Based on this situation, a global pandemic was instituted by the World Health Organization resulting many restrictions and rising many problems for the medical systems all around the world. In this paper we would like to present some of the applications of modern technologies that helped, in way or another, in the fight against the pandemic, being sometimes a helping hand in the global fight against this virus. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study. [ABSTRACT FROM AUTHOR]

This paper finds that although containment policies can successfully reduce the spread of the virus, they can also have a substantial impact on reducing mobility and economic activity. It also shows that testing, combined with effective contact tracing, is crucial in reducing the spread of the virus, especially at relatively low levels of infection, and that mask-wearing and the protection of the elderly population in general and those in care homes in particular, might play an important role in combatting the virus while minimizing economic costs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted life as usual around the globe. Efforts to control the spread of the virus with lockdowns and border closures pushed millions of people into food and social insecurity. Most research on humanitarian organizations have been dominated by the uncertainty and urgency of disaster response operations. However, some humanitarian organizations also operate in long-term continuous aid programs where efficiency is the key goal. We analyzed the operations of food banks in the Feeding America network and The Salvation Army USA, and found them to be ambidextrous organizations. The ambidextrous humanitarian organizations like food banks and Salvation Army, focus on long-term continuous aid programs, specifically pertaining to the sustenance of the communities they serve, but also play a key part as first responders or as local agencies aiding in disaster relief and response. We propose a framework to analyze disaster, development, and sustenance aid supply chains, and identify future research opportunities. [ABSTRACT FROM AUTHOR]

Many countries have experienced multiple waves of infection during the COVID-19 pandemic. We propose a novel but parsimonious extension of the SIR model, a CSIR model, that can endogenously generate waves. In the model, cautious individuals take appropriate prevention measures against the virus and are not exposed to infection risk. Incautious individuals do not take any measures and are susceptible to the risk of infection. Depending on the size of incautious and susceptible population, some cautious people lower their guard and become incautious—thus susceptible to the virus. When the virus spreads sufficiently, the population reaches "temporary" herd immunity and infection subsides thereafter. Yet, the inflow from the cautious to the susceptible eventually expands the susceptible population and leads to the next wave. We also show that the CSIR model is isomorphic to the SIR model with time-varying parameters. [ABSTRACT FROM AUTHOR]

For all humanity, the sudden outbreak of Corona Virus Disease 2019 has been an important problem. Timely and effective media coverage is considered to be one of the effective approaches to control the spread of epidemic in early stage. In this paper, a Sentiment‐enabled Susceptible‐Exposed‐Infected‐Recovered (SEIR) model is established to reveal the relationship between the propagation of the epidemic and media coverage. The authors take the positive and negative media coverage into consideration when implementing the Sentiment‐enabled SEIR model. This model is constructed by parameterizing the number of current confirmed cases, cumulative cured cases, cumulative deaths, and media coverage. The numerical simulation and sensitivity analysis are conducted based on the Sentiment‐enabled SEIR model. The numerical analysis confirms the rationality of the Sentiment‐enabled SEIR model. The sensitivity analysis shows that positive media coverage acts a pivotal part in reducing the figure for confirmed cases. Negative media coverage has an effect on the figure for confirmed cases is not as significant as that of positive media coverage, but it is not negligible. [ABSTRACT FROM AUTHOR]

Since the rise of the COVID-19 pandemic, safeguarding public health has taken paramount importance, drastically transforming lives in unprecedented ways. Mitigating the virus's spread necessitates various measures, including wearing face masks. However, there has been a concerning trend of laxity and incorrect use of masks in public spaces, heightening the fear of escalating cases globally. Numerous deep-learning models have been proposed for face mask detection. This paper presents s novel DenseMaskNet deep learning model which integrates real-time visualization with transfer learning techniques. This involves a two-step process. The first step is mask detection, which is built by fine-tuning the state-of-the-art pre-trained deep learning model, DenseNet201. The Face- Mask Recognition Dataset (FMRD), which is free from biases and contains masked, unmasked, incorrect masked faces, is utilized in the mask detection process. In the second step, Face detection and real-time visualization, prediction scores calculated for each image of the FMRD dataset are fed into a res10_300 × 300_ssditer_140000_caffemodel, a pre-trained single-shot MultiBox face detector model, which combines these scores to enable real-time face mask detection. The OpenCV library is utilized to capture real-time video from a camera and experiment with our model in practical scenarios. Comparative analysis against VGG16, InceptionV3, ResNet50, MobileNetV2, and Efficient-NetB7 reveals that DenseNet201 consistently outperforms all metrics. Notably, DenseMaskNet exhibits superior performance with precision, F1 score, recall, and accuracy of 98%, 98%, 99%, and 99%„ respectively. These compelling results affirm the efficacy of DenseMaskNet for real-time face mask detection, suggesting its viability for widespread utilization in real-world settings. [ABSTRACT FROM AUTHOR]

This paper shows the outcomes of computer virus propagation (CVP) model, represented with susceptible, exposed, infected, quarantine and recovered computers (SEIRQ), classes based mathematical model using the stochastic procedures. The systematic study of the CVP based SEIRQ model represents that the equilibrium state of virus-free is stable globally with reproduction not more than one, while the viral symmetry is attractive globally. The numerical performances of the CVP based SEIRQ model are presented by using the stochastic computational framework based on the artificial neural networks (ANNs) together with the Levenberg-Marquardt backpropagation (LBMB) called as ANNs-LBMB. The learning procedures via ANNs-LBMB for solving the CVP based SEIRQ model are implemented to indorse the statics using the testing, authorization, and training. Thirteen numbers of neurons and the data selection for training 72%, testing 12% and validation 16% are selected to solve the model. For the numerical outcomes of the CVP based SEIRQ model using the ANNs-LBMB, a dataset is considered through the Adams approach. The accuracy and reliability performances of the scheme are presented by using the values of the absolute error (AE) along with the observations of state transitions (STs), regression, mean square error (MSE) and error histograms (EHs). [ABSTRACT FROM AUTHOR]

Quick and highly sensitive detection of the SARS-CoV-2 virus can efficiently reduce the possibility of viral spreading. In this study, a differential-phase surface plasmon resonance (SPR) biosensor is constructed to directly detect the SARS-CoV-2 virus. The SARS-CoV-2 spike protein antibodies are applied as bioprobes and functionalized on Au film, forming a sensing surface. A numerical analysis of the phase shifts of the SPR interferogram, termed the index of phase shifts (IoPS), is presented, which does not depend on a reference beam or an s-component beam. The IoPS can sensitively reflect small changes in the refractive index (as low as 3.75 × 10−10 RIU) when NaCl solution is used as an analyte. The limit of detection (LOD) of the proposed SPR biosensor in detecting the SARS-CoV-2 S protein is 1 ag mL−1. In comparison with a SARS-CoV-2 negative control (NC) sample collected from healthy people, which has an IoPS of ΔφNC = 39°, the SPR biosensor shows a very strong response to the SARS-CoV-2 virus collected from infected people with a phase shift of ΔφIoPS = 566°. To further improve the detection sensitivity, a sandwich structure is designed by applying SARS-CoV-2 spike protein antibody functionalized gold nanoparticles (anti-S@AuNPs). This reduces the LOD for the detection of the SARS-CoV-2 S protein to 0.1 ag mL−1. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate how to interpret the outcome of a test‐negative design study. We does so by systematically reviewing the properties of the design in relation to its potential applications. We first argue that the application of the design does not depend on certain assumptions (as is sometimes articulated in the literature), which may open new opportunities for using the design. Then, we relate a number of limitations of the design. The design cannot be used for studying the mortality effects of vaccines and is problematic for studies into the effect on hospitalization. The vaccine's effectiveness on the transmission of viruses is also potentially problematic, depending very much on the characteristics of the tests. The implication of our findings is that the test‐negative designs can, at best, be seen as an indication of effectiveness in highly idealized situations that are often far away from reality. [ABSTRACT FROM AUTHOR]

The spreading of COVID-19 became a global issue that had a significant impact on health, life, and economic sectors. Efforts from all over the world are focused on discussing a variety of healthcare approaches to reduce the effect of COVID-19 among individuals. Mathematical tools with numerical simulations are important approaches that help international efforts to determine critical transmission factors as well as controlling the virus spread. In this paper, we develop a mathematical model that considers a vaccination compartment in terms of ordinary differential equations. This study focuses on the real data of confirmed cases in Kurdistan Region of Iraq from July 17th, 2021 to January 1st, 2022. Model results and real data for the total number of infected people were compared using computational tools in MATLAB. Additionally, non-normalization, half-normalization, and full-normalization methods are used to determine the local sensitivities between model variables and parameters. Interestingly, computational results show that the dynamics of model results and real confirmed cases are very close to each other. Accordingly, the elasticity coefficients provide a great understanding of the impact of vaccination on transmissions. The model results here can also help international efforts for further suggestions and improvements to control this disease more effectively [ABSTRACT FROM AUTHOR]

The presence of COVID-19 infection and its threat to human life has led to the urgent need to contribute to combating it, so global initiatives have emerged to regulate and manage the virus, with the aim of eliminating it once and for all. Since the use of image processing has succeeded in solving many complicated problems in the medical field, it can now be employed to aid in the fight against COVID-19 and control it by extracting the relevant features collected from medical imaging datasets and categorizing using machine learning and deep learning approaches in order to diagnose and predict the COVID-19 disease. This study provides an overview of current findings on machine learning methods that have been applied to COVID-19 and aspects related to the types of algorithms, the medical dataset used, and the accuracy of researchers' results. This paper, review the latest proposed models for COVID-19 classification and diagnosis applied to CT scans and chest X-ray images to provide further insight into disease reduction. COVID-19 diagnosis is critical for identifying an infected person and preventing the virus from spreading. Because diffusion is rapid, an automated rapid diagnostic mechanism is required to deal with a huge population so image processing methods are the most suitable technology for this. These models' accuracy ranged from 80.6% to 100%, demonstrating that image-processing methods can be used to assess and diagnose COVID-19 clinically. [ABSTRACT FROM AUTHOR]