ABSTRACT
The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.
ABSTRACT
With the explosion of COVID-19 cases and the government's needs to control virus spreading, the development of effective and robust systems for managing vaccination certificates to restrict citizens' activities has been in the centre of many governments. This paper proposes a system that allows for the update of the status of certificates and bases its function on a specific form of logs stored on Blockchains and a set of rules for the interpretation of these logs. Also an outline of a proof of concept implementation of the system in Ethereum together with a cost and security analysis are provided in the paper. The proposed architecture provides several benefits with the most prominent one being the suspension of certificates in case an already vaccinated individual is found positive. In existing certificate management systems a vaccinated individual that is tested positive still holds a valid vaccination certificate during the self-isolation period. This vulnerability allows infected individuals to commute freely and thus facilitates the spread of the pandemic. The proposed solution is not limited to COVID-19 related certificates, but rather it could be deployed in any kind of digital certificate. © 2022 ACM.
ABSTRACT
Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people's congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well. © 2022 Lavoisier. All rights reserved.
ABSTRACT
The Covid-19 disease is caused by the severe acute respiratory (SAR) syndrome coronavirus-2 and becomes the reason for the Global Pandemic since 2019. Until July 2022, the total reported cases were 572 million and reported deaths were 6.38 million around the world. In many countries the infections caused severe damages. It not only took the precious lives but also caused few other national damages like economic crisis. The only solution to stop this pandemic is to increase the vaccination and reducing the spreads. The covid 19 virus is an airborne disease and spread when people breathe virus contaminated air. The WHO and all the nations were insisting to maintain social distance to control the virus spreading. But maintaining the social distance in public places is very hard. In this project we developed a method for detecting social distance. The system uses Raspberry Pi processor to detect the distance between two people from the live video stream. The YOLOv3 technique is used to detect the object from single frame of the video. © 2022 IEEE
ABSTRACT
In this article, we analysed the situation during the pandemics of COVID-19 virus in the Slovak Republic. We summarized measures within transport system, that Slovak Republic took in an attempt to soften the impact of the virus and to minimize its spread. We found out that these measures and the swiftness of their adoption had strong influence on flattening the curve of virus spreading. The main contribution of this article is in deeper look into possibilities of a smart transport system, aimed to identify, what more could the smart transport system offer to help in a fight of the country against spreading virus. For this purpose, we need to remind our previous work, where we described concept Smart City, concept Safe City, and their systems. One of these systems is system smart transport, and its description in previous work was the base ground for our design of additional solutions, improving safety in the time of pandemics. Therefore, this article will start with description of system Safe City and system smart transport, followed by examination of the case, evaluation of adopted measures, and proposal of additional measures. The focus of proposed measures will be given to the original design of mass transport system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has affected hundreds of millions of people in countries around the world. The number of new cases has reached 100,000 per day since the last wave of COVID-19 in Vietnam. It has become very apparent that the front-line employees are overworked. There are not enough PCR tests to keep up with the rate of the virus spreading in our community. In addition, the PCR test is expensive for the government, highly invasive, and time-consuming for patients, which discourages individuals from visiting the clinic for testing. Therefore, it is very necessary to have a quicker and simpler way of prescreening patients. This is the reason why the paper will introduce a new artificial intelligence application, named COVCOUGH, to early detect COVID-19 patients using cough sounds recorded by smartphones. During the recent peak of the epidemic in Vietnam, the COVCOUGH has been deployed and has more than 10,000 users. © 2022 IEEE.
ABSTRACT
In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In this letter, we consider an epidemic model for two competitive viruses spreading over a metapopulation network, termed the ‘bivirus model’for convenience. The dynamics are described by a networked continuous-time dynamical system, with each node representing a population and edges representing infection pathways for the viruses. We survey existing results on the bivirus model beginning with the nature of the equilibria, including whether they are isolated, and where they exist within the state space with the corresponding interpretation in the context of epidemics. We identify key convergence results, including the conclusion that for generic system parameters, global convergence occurs for almost all initial conditions. Conditions relating to the stability properties of various equilibria are also presented. In presenting these results, we also recall some of the key tools and theories used to secure them. We conclude by discussing the various open problems, ranging from control and network optimization, to further characterization of equilibria, and finally extensions such as modeling three or more viruses. IEEE
ABSTRACT
Covid-19 is a highly contagious virus spreading all over the world. It is caused by SARS-CoV-2. virus. Some of the most common symptoms are fever, cough, sore throat, tiredness, and loss of smell or taste. There are two types of tests for COVID-19: the PCR test and the antigen test. Automatic detection of Covid-19 from publicly available resources is essential. This paper employs the commonly available chest x-ray (CXR) images in the classification of Covid-19, normal and viral pneumonia cases. The proposed method divides the CXR images into subblocks and computes the Discrete Cosine Transform (DCT) for every subblock. The DCT energy compaction capability is employed to produce a compressed version for each CXR image. Few spectral DCT components are incorporated as features for each image. The compressed images are scanned by average pooling windows to reduce the dimension of the final feature vectors. A multilayer artificial neural network is employed in the 3-set classification. The proposed method achieved an average accuracy of 95 %. While the proposed method achieves comparable accuracy relative to recent state-of-the-art techniques, its computational burden and implementation time is much less. © 2022 IEEE.
ABSTRACT
One of the main policies to contain a pandemic spreading is to reduce people mobility. However, it is not easy to predict its actual impact, and this is a limitation for policy-makers who need to act effectively and timely to limit virus spreading. Data are fundamental for monitoring purposes;however, models are needed to predict the impact of different scenarios at a granular scale. Based on this premise, this paper presents the first results of an agent-based model (ABM) able to dynamically simulate a pandemic spreading under mobility restriction scenarios. The model is here used to reproduce the first wave of COVID-19 pandemic in Italy and considers factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns. The model is calibrated with real data (considering the first wave), and it is based on a combination of static and dynamic parameters. First results show the ability of the model to reproduce the pandemic spreading considering the lockdown strategy adopted by the Italian Government and pave the way for scenario analysis of different mobility restrictions. This could be helpful to support policy-making by providing a strategic decision-tool to contrast pandemics. © 2021 Copyright for this paper by its authors.
ABSTRACT
This research explores how exterior public space - defined through the configuration of the city - and human behavior affect the spread of disease. In order to understand the virus spreading mechanism and influencing factors of the epidemic which accompany residents' movement, this study attempts to reproduce the process of virus spreading in city areas through computer simulation. The simulation can be divided into residents' movement simulation and the virus spreading simulation. First, the Agent-based model (ABM) can effectively simulate the behavior of the individual and crowd;real location data - uploaded by residents via mobile phone applications - is used as a behavioral driving force for the agent's movement. Second, a mathematical model of infectious diseases is constructed based on SIR (SEIR) Compartmental models in epidemiology. Finally, by analyzing the simulation results of the agent's movement in the city and the virus spreading under different conditions, the influence of multiple factors of city configuration and human behavior on the virus spreading process is explored, and the effectiveness of countermeasures such as social distancing and lockdown are further demonstrated. © Association for Computer Aided Design in Architecture Annual Conference, ACADIA 2021.
ABSTRACT
The most pernicious ongoing phenomenon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the disease is known as Covid-19 that has spread worldwide, has set the world in a state of torpid. In the interim, to understand every possible potential related to the virus and its transmission is getting studied by the researchers with every possible effort. Although finding causal relations among openly accessible data and the target phenomenon in a scientific manner is important, it would be helpful when we find potentially strongly related parameters to be scientifically further investigated. To contribute towards, in this paper we have presented our case study on collecting and measuring openly available data, the local tem-perature in the area and its prediction performance to virus spreading. © 2021 IEEE.
ABSTRACT
In this article, we analysed the situation during the pandemics of COVID-19 virus in the Slovak Republic. We summarized measures within transport system, that Slovak Republic took in an attempt to soften the impact of the virus and to minimize its spread. We found out that these measures and the swiftness of their adoption had strong influence on flattening the curve of virus spreading. The main contribution of this article is in deeper look into possibilities of a smart transport system, aimed to identify, what more could the smart transport system offer to help in a fight of the country against spreading virus. For this purpose, we need to remind our previous work, where we described concept Smart City, concept Safe City, and their systems. One of these systems is system smart transport, and its description in previous work was the base ground for our design of additional solutions, improving safety in the time of pandemics. Therefore, this article will start with description of system Safe City and system smart transport, followed by examination of the case, evaluation of adopted measures, and proposal of additional measures. The focus of proposed measures will be given to the original design of mass transport system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Traditional epidemic models, like the classical SIR, are fitted to real data using deterministic optimization techniques. As a consequence, their performances cannot be properly assessed and, more importantly, the estimates of the critical epidemic parameters (which are of dramatic importance in monitoring the epidemic evolution) cannot be complemented with the calculation of confidence intervals. The aim of the present work is to remove such limitations and to compare the results obtained using two stochastic versions of deterministic SIR models. We describe the two alternatives and the associated estimation procedures, and we apply the two methodologies to a set of COVID-19 data observed in Italy in the 2020 pandemic wave. Our estimates of the basic reproduction number are comparable with the official sources, but using our methods uncertainty can also be properly assessed. © 2022 by Emerald Publishing Limited.
ABSTRACT
The COVID-19 virus has taken over the course of the world for over two years;governments all over the world have been trying to mitigate its effects in several ways such as instilling most jobs to be done at home instead of working from the office. Thus, it is important to be able to see predictions of COVID-19 cases to better plan the intervention of the virus spreading. With the use of machine learning, our paper aims to propose and evaluate an LSTM (Long Short Term Memory) model that can forecast daily COVID-19 cases in Indonesia. Several tests show that 50 epochs and a batch size of eight are the best parameters to use for our model. Furthermore, after comparison with differing amounts of lookbacks, we have decided that 10 is best for our model as it consistently does better than other numbers of lookbacks. Based on our model, there will still be an increase of COVID-19 cases in the future. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has already spread over 200 countries in a few months and taken a toll on many lives. At this critical time, there is a need to follow some precautions to control the virus spreading rapidly through direct and indirect contact. The World Health Organization (WHO) has already recommended the importance of face masks for protection from the virus. Hence, one of the prime changes we have had to incorporate in our lives is wearing a face mask. This work reports the development of Ag particles containing polydimethylsiloxane (PDMS) based e-skin sensor, which generates signals on touch (contact mode) or proximity (non-contact mode) near the sensor. These signals are retrieved using IoT. The signals indicate a person's presence, which activates face mask detection using deep learning. This model is an IoT and Machine Learning-based system. When a human touches or places a hand near the PDMS-Ag sensor, this model performs face masks detection. This model is also suitable for security purposes. Since controlling the number of new COVID-19 cases is the need of the hour, we are using face mask detection in this study. © 2021 IEEE.
ABSTRACT
Artificial Intelligence (AI), since the onset of the COVID-19 pandemic at the beginning of the last year, is playing an important role in supporting physicians and health authorities in different difficult tasks such as virus spreading, patient diagnosing and monitoring, contact tracing. In this paper, we provide an overview of the methods based on AI technologies proposed for COVID-19 forecasting. Summary statistics of the techniques adopted by researchers, categorized on the base of the underlying AI sub-area, are reported, along with publication venue of papers. The effectiveness of these approaches is investigated and their capabilities or weaknesses in providing reliable predictions are discussed. Future challenges are finally analyzed and research directions for improving current tools are suggested. © 2021 ACM.
ABSTRACT
India is one of the countries in the world which is badly affected by the COVID-19 second wave. Assembly election in four states and a union territory of India was taken place during March-May 2021 when the COVID-19 second wave was close to its peak and affected a huge number of people. We studied the impact of assembly election on the effective contact rate and the effective reproduction number of COVID-19 using different epidemiological models like SIR, SIRD, and SEIR. We also modeled the effective reproduction number for all election-bound states using different mathematical functions. We separately studied the case of all election-bound states and found all the states showed a distinct increase in the effective contact rate and the effective reproduction number during the election-bound time and just after that compared to pre-election time. States, where elections were conducted in single-phase, showed less increase in the effective contact rate and the reproduction number. The election commission imposed extra measures from the first week of April 2021 to restrict big campaign rallies, meetings, and different political activities. The effective contact rate and the reproduction number showed a trend to decrease for few states due to the imposition of the restrictions. We also compared the effective contact rate, and the effective reproduction number of all election-bound states and the rest of India and found all the parameters related to the spread of virus for election-bound states are distinctly high compared to the rest of India.
ABSTRACT
The COVID-19 outbreak has generated, in addition to the dramatic sanitary consequences, severe psychological repercussions for the populations affected by the pandemic. Simultaneously, these consequences can have related effects on the spread of the virus. Pandemic fatigue occurs when stress rises beyond a threshold, leading a person to feel demotivated to follow recommended behaviours to protect themselves and others. In the present paper, we introduce a new susceptible-infected-quarantined-recovered-dead (SIQRD) model in terms of a system of ordinary differential equations (ODE). The model considers the countermeasures taken by sanitary authorities and the effect of pandemic fatigue. The latter can be mitigated by fear of the disease's consequences modelled with the death rate in mind. The mathematical well-posedness of the model is proved. We show the numerical results to be consistent with the transmission dynamics data characterising the epidemic of the COVID-19 outbreak in Italy in 2020. We provide a measure of the possible pandemic fatigue impact. The model can be used to evaluate the public health interventions and prevent with specific actions the possible damages resulting from the social phenomenon of relaxation concerning the observance of the preventive rules imposed.
ABSTRACT
Cell-cell fusion between eukaryotic cells is a general process involved in many physiological and pathological conditions, including infections by bacteria, parasites, and viruses. As obligate intracellular pathogens, viruses use intracellular machineries and pathways for efficient replication in their host target cells. Interestingly, certain viruses, and, more especially, enveloped viruses belonging to different viral families and including human pathogens, can mediate cell-cell fusion between infected cells and neighboring non-infected cells. Depending of the cellular environment and tissue organization, this virus-mediated cell-cell fusion leads to the merge of membrane and cytoplasm contents and formation of multinucleated cells, also called syncytia, that can express high amount of viral antigens in tissues and organs of infected hosts. This ability of some viruses to trigger cell-cell fusion between infected cells as virus-donor cells and surrounding non-infected target cells is mainly related to virus-encoded fusion proteins, known as viral fusogens displaying high fusogenic properties, and expressed at the cell surface of the virus-donor cells. Virus-induced cell-cell fusion is then mediated by interactions of these viral fusion proteins with surface molecules or receptors involved in virus entry and expressed on neighboring non-infected cells. Thus, the goal of this review is to give an overview of the different animal virus families, with a more special focus on human pathogens, that can trigger cell-cell fusion.