ABSTRACT
Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.
ABSTRACT
Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.
ABSTRACT
Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.
ABSTRACT
The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.
ABSTRACT
This paper aims to determine relationships between 160 matches statistics and the match results in two match stages of 2020 CSL under the COVID-19 pandemic prevention and control. A team's winning probability was evaluated by a two-standard-deviation increase in the value of each variable. The smallest worthwhile change was used to evaluate nonclinical magnitude-based inferences. The results showed that for group round robin stage, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Set Piece, Cross Accuracy, Counterattack, Won Challenge, Tackle Gaining, HIR Distance in BP, Sprinting Distance in BP), two had obviously negative effects (Distance Covered in Penalty Area, Sprinting Distance Out of BP), other twenty-three statistics had either trivial or unclear effects. While for the knockout stage, the effects of nine match statistics (Pass Accuracy, Forward Pass Accuracy, Delivery into Attacking Third, Delivery into Penalty Area, Dribble into Attacking Third, Corner, Foul Committed, Yellow Card, Distance Covered in Attacking Third) turned to clearly positive, the effects of Won Challenge, Cross Accuracy turned to trivial and clearly negative, respectively. Coaches and players should take these different aspects into account when planning practices and competitions for their teams. © 2023 SPIE.
ABSTRACT
At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.
ABSTRACT
The COVID-19 epidemic is running at a high level globally, affecting all aspects of society, and medical education is no exception. With the rapid development of medical science, continuing medical education is an important way for medical workers to receive lifelong education. Meanwhile, attending continuing medical education is an inevitable requirement to ensure clinical ability. Under the background of normalization of epidemic prevention and control and the new situation of medical development, the management of continuing medical education in hospitals must follow the current situation and keep pace with the times. Therefore, the Internet support system to continuing education has emerged. This study used PDSA method to explore the construction of the regional center of continuing medical education through Internet under the background of normalization of epidemic prevention and control, aiming to promote the integration of medical education resources under the new situation, expand the learning channels of medical staff, and improve the level of medical education and teaching. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic, which began in December 2019, has brought huge changes to people's lives. In terms of tourism, the prevention and control measures taken to stop the spread of the epidemic have led to a decline in the number of global trips, and the development of the tourism economy has entered a trough. The economic losses caused by the tourism industry and its corresponding service industry need to be resolved urgently. At the same time, with the development of artificial intelligence, virtual reality and other technologies, the concept of smart tourism was proposed. Based on this, we put forward a website platform model for tourists to inquire about tourism, which takes Wudang Mountain as an example and uses artificial intelligence as technical support. This platform model can meet the needs of users to carry out cloud tour of scenic spots online and enjoy scenic spots without leaving home. It can also conduct intelligent query of offline scenic spots, including route customization, ticket ordering, scenic spot recommendation and many other contents, to meet the various needs of tourists. In addition, the highlight of the platform model is the guide assistant that can conduct dialogues. Based on artificial intelligence technology, it can solve users' specific problems and give feasible solutions in the process of dialogues with users. © 2023 IEEE.
ABSTRACT
In view of the fact that Covid-19 is highly contagious, which poses great threat and inconvenience to people's production and life, a multifunctional robot control system with single-chip microcomputer as the control core is designed, aiming at the problems of centralized isolation points in communities, complicated situation and difficult management. Firstly, Gmapping algorithm is used to realize the robot's autonomous positioning and avoidance. Secondly, a three-degree-of-freedom robot arm is designed to disinfect any indoor space. Finally, Gmapping algorithm is used to recognize and measure the temperature of human face. Through the simulation experiment, this method can improve the efficiency of searching the shortest path and carry out disinfection work while reducing human contact, improving public safety and has practical value. © 2023 IEEE.
ABSTRACT
Since the outbreak of Corona Virus Disease 2019, it has had a significant impact on people's lives. In order to help the government grasp the social opinion and do more scientific and practical propaganda and public opinion guidance for prevention and control, and to fully reflect people's attitude toward the epidemic and provide data support for government departments to release epidemic prevention measures. This paper uses Corona Virus Disease 2019-related Weibo comments as the research object and analyzes their sentiment using deep learning algorithms. The number of characters in Weibo comments is usually less than 140, which belongs to the category of short texts. Due to the use of few words, random user language, and irregular grammar, these texts have poor performance in text separation and word vector expression, adversely affecting sentiment classification. In order to solve this problem, this paper constructs the BERT-DPCNN model for sentiment analysis of epidemic short texts, which can not only extract the sentence-level text dependencies but also effectively avoid the problem of gradient disappearance of deep neural networks. The experiments show that the BERT-DPCNN model has the best effect and is of great value for the sentiment classification of short epidemic text. © 2022 IEEE.
ABSTRACT
The world has seen many pandemics in the past. COVID-19, SARS, and H1N1 are some of them. During the period of epidemic prevention and control, tracing the source becomes a challenge to control the disease, and contact tracing applications are developed by many countries to slow down the spread of pandemics. However, the privacy problem is becoming one of the important issues in contact tracing systems nowadays. To protect the private information for infected persons and their potential contacts in the scenario which described in our paper, the effective encryption key sharing method can be applied to contact tracing systems. In this paper, we propose a key sharing mechanism for contact tracing application, it is allows a confirmed patient to hide their sensitive information from others when send the notification messages. Our mechanism is used to achieve such a user's privacy functionality. We present the security analysis and prove the security of the mechanism. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The financial industry is a high-risk industry. Once the financial industry risk happen, it will affect the economic development. Ensuring the safe, efficient and steady operation of finance and preventing systemic financial risks are the urgent needs of China's opening up to the outside world and building a well-off society in an all-round way. Stable and efficient economic development is the basis of financial risk prevention and control, which is the inherent requirement of high-quality economic development. Strengthening macro-prudential management has become the core content of financial regulatory reform in major international organizations and economies after the international coronavirus outbreak and preventing systemic financial risks is the fundamental goal of macro-prudential management. This paper takes the assessment and monitoring of China's systemic financial risks as the research object, and proposes an assessment algorithm of systemic financial risks based on risk data fuzzy clustering analysis. The established financial systemic risk measurement method can identify risks to a certain extent, deeply understand the nature, root and key areas of systemic financial risks, and build a long-term mechanism to prevent and resolve systemic financial risks. © 2022 IEEE.
ABSTRACT
Against the backdrop of the ongoing COVID-19 pandemic, We propose FMRS-CFR (Face mask recognition system-Centerface Resnet), a mask recognition system for epidemic prevention and control based on multi-algorithm fusion to adapt to multi-scenario applications. In this work, Centerface face key point detection and Resnet50 classification model were used. Built a system that maintains multi-adaptation with the dynamics of external scenarios and ported the system to the Atlas 200 Developer Kit, And quantitative evaluation of videos in more than a dozen different scenarios. Experimental results show that the FMRS-CFR system can achieve a recognition accuracy rate of 99.88%, which greatly improves the recognition rate of not wearing a mask or wearing the correct one to a certain extent, and achieves the purpose of effectively assisting epidemic prevention and control. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Mask detection plays a major role in the prevention and control of epidemics after the COVID-19 outbreak as it is the most practical and effective method of prevention. For the appropriate employees, a great automatic real-time face mask identification system based on deep learning can significantly lessen work-related stress. The systems for mask identification that are currently in use, however, are largely resource-intensive and do not strike a reasonable balance between speed and accuracy. In our system, the mask detector is SSD, and to extract the image's features and decrease a number of parameters, MobileNet takes the role of VGG-16. Pre-trained models from other domains are transferred to our model using transfer learning techniques. © 2023 IEEE.
ABSTRACT
The ravages of the COVID-19 and the continuous mutation of the COVID-19 make this war without gunpowder smoke unstoppable. With the continuous epidemic prevention and control, the resident closure and isolation by community is an effective way to block the large-scale development of the COVID-19. However, the shortage of daily necessities for residents during the lockdown period requires timely arrangements and deployment by local departments to ensure the basic living of the residents under lockdown. A necessary distribution and circulation system in the epidemic prevention and control community was designed and developed in this paper. The proposed necessity distribution and circulation system is mainly to help the government distribute supplies to residents and the circulation of necessities between residents more efficiently. The design process of the system includes the software development process of demand analysis, overall design, detailed design and programming;it was adopted CS three-tier architecture software development mode and the software development technology of .Net + SQLSERVER. The main business modules of the system are including necessity circulation between residents, government supply management, and volunteer necessity delivery business. The system can also be applied to the necessity circulation subsystem of the community healthy life service platform for the daily life of residents. © 2022 ACM.
ABSTRACT
Under the background of normalized COVID-19 prevention and control, regional epidemics occur frequently in China. How to quantify the impact of COVID-19 prevention and control measures on economic operation and passenger and freight transportation has become an urgent problem. To this end, we design a calculation method for expressway transportation indicators, propose the level and stage division process of COVID-19 prevention and control measures, and then establish a difference-in-difference model to further analyze their impact on expressway transportation indicators. Taking major cities in the Guangdong-Hong Kong-Macao Greater Bay Area as an example, case studies are conducted based on the expressway toll data and COVID-19 prevention and control information from May 2020 to April 2022. The results show that in the level I (strengthened) stage, the passenger vehicle flow has dropped significantly, the drop in each case is between 8% and 27%, and the freight indicators have not changed significantly. In Shenzhen and Dongguan, both passenger and freight indicators dropped sharply in the level II (strict) stage. Passenger vehicle flow in the two cities dropped by 46.3% and 33.7%, and truck flow by 42.7% and 27.6%, respectively, and cargo and turnover decreased as much as truck flow. The average inter- city distance of expressway passenger cars has a downward trend under the level I stage, but under the level II stage, the average inter-city distance of passenger cars and trucks has increased significantly. This study can provide a certain reference value for the formulation and implementation of COVID-19 prevention and control measures in cities and urban agglomerations. © 2022 Science Press. All rights reserved.
ABSTRACT
For epidemic prevention and control, molecular diagnostic techniques such as field-effect transistor (FET) biosensors is developed for rapid screening of infectious agents, including Mycobacterium tuberculosis, SARS-CoV-2, rhinovirus, and others. They obtain results within a few minutes but exhibit diminished sensitivity (<75%) in unprocessed biological samples due to insufficient recognition of low-abundance analytes. Here, an electro-enhanced strategy is developed for the precise detection of trace-level infectious agents by liquid-gate graphene field-effect transistors (LG-GFETs). The applied gate bias preconcentrates analytes electrostatically at the sensing interface, contributing to a 10-fold signal enhancement and a limit of detection down to 5 × 10−16 g mL−1 MPT64 protein in serum. Of 402 participants, sensitivity in tuberculosis, COVID-19 and human rhinovirus assays reached 97.3% (181 of 186), and specificity is 98.6% (213 of 216) with a response time of <60 s. This study solves a long-standing dilemma that response speed and result accuracy of molecular diagnostics undergo trade-offs in unprocessed biological samples, holding unique promise in high-quality and population-wide screening of infectious diseases. © 2023 Wiley-VCH GmbH.
ABSTRACT
The focus of this contribution is to show how the course of the pandemic can be retrospectively investigated in terms of change points detection. At this aim, an automatic method based on recursive partitioning is employed, considering the time series of the 14-day notification rate of newly reported COVID-19 cases per 100,000 population collected by the European Centre for Disease Prevention and Control. The application shows that the pandemic, at the individual country level, can be broken into different periods that do not correspond to the common notion of wave as a natural pattern of peaks and valleys implying predictable rises and falls. This retrospective analysis can be useful either to evaluate the implemented measures or to define adequate policies for the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Emergencies have a significant impact on the economy. Thus, effective prevention and control measures can reduce economic losses to the greatest extent. Taking the novel coronavirus outbreak as the starting point, we proposed the SEEIR-E model, an optimization model of the SEIR. The model compares two different prevention and control modes, 'fence mode' and 'free-range mode' Combined with the big data of the epidemic, systematic simulation and analysis were carried out in the Netlogo simulation environment. When environmental factors are the same, the 'fence model' can control the spread of the disease more quickly, and the economic impact is less. At the same time, the impact of various environmental factors on economic recovery was simulated and analyzed. The result provides a basic understanding in economic recovery after the epidemic. © 2022 IEEE.
ABSTRACT
Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.