ABSTRACT
Nowadays, spatial geographic data analysis and GIS related software are more and more applied to the planning of urban public facilities. Under the COVID-19, people pay more attention to the protection of medical facilities for people's health, and a reasonable distribution of hospital facilities is conducive to people's health. Taking Haikou City as an example, this research will optimize the location of hospital space layout according to the existing third-level first-class general hospitals in Haikou City by using GIS software, road analysis, spatial analysis, and other methods. The results show that the existing hospitals in Haikou are too concentrated in the central urban area, the overall distribution of medical facilities is lack of balance, and there is a serious lack of medical facilities in new urban development areas and suburbs. According to the comparison between population density analysis and traffic analysis and the service scope of existing hospitals, the author finds out the scope of hospitals that need to be supplemented, and then calculates the scope of service area after taking several random points within the scope, and finally finds the one with the largest service scope is the optimal location. The results obtained by optimizing the site selection can provide a scientific reference for the rational layout of medical facilities in Haikou City in the future. © 2023 SPIE.
ABSTRACT
As COVID-19 has swept across the world, the escalating number of confirmed and suspected cases overwhelmed the admission capacity of the designated hospitals. Faced with such a grim situation, governments made a quick decision to build emergency medical facilities to address the outbreak. However, the emergency medical facilities faced a huge risk of epidemic spread and improper site could lead to serious secondary transmission. Using the disaster prevention and risk avoidance function of urban green space can solve the problem of selecting the location of emergency medical facilities to a certain extent, with country parks having a high degree of compatibility with the latter. Based on the location requirements of emergency medical facilities, using Analytic Hierarchy Process and Delphi method, through analyzing the type of country parks, effective risk avoidance area, spatial fragmentation, distance from water sources, wind direction, and distance from the city, quantification of 8 impact factors such as hydrogeology and traffic duration was conducted to comprehensively compare 30 country parks in Guangzhou. The results showed that the overall quality of country parks approximated a normal distribution, with Lianma Forest Country Park having the highest comprehensive score and the most balanced distribution of scores for various impact factors. Considering safety, expandability, rehabilitation, convenience, pollution prevention, and fecal isolation, it is a preferred destination for emergency medical facility construction.
Subject(s)
COVID-19 , Public Health , Humans , Parks, Recreational , Emergencies , Cities , China , Public FacilitiesABSTRACT
The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Escherichia coli O157:H7 is a major cause of foodborne disease outbreaks throughout the world, while methicillin-resistant Staphylococcus aureus (MRSA) is responsible for many difficult-to-treat infections in humans. Ultraviolet (UV) irradiation is commonly used for disinfection in food processing, medical facilities, and water treatment to prevent the transmission of these pathogen. With increased use of UV disinfection technologies over the last few years because of COVID-19 and concerns about other communicable disease, it has become a concern that microbial species could develop tolerance to UV irradiation, especially when it is applied continuously. To elucidate the effect of continuous UV exposure at different wavelengths and power levels on the tolerance development of bacteria, Escherichia coli O157:H7 and MRSA)USA300 growths were investigated by continuously exposing inoculated agar plates to six different commercially available UV sources at wavelengths of 222 nm, 254 nm, 275 nm, and 405 nm. The agar plates in these experiments were partially covered by a thin acrylic sheet, which provided either complete protection from the UV to the cells directly under the sheet, no protection if significantly away from the sheet, or partial protection near the edges of the sheet due to shading or small amounts of UV reflection under the sheet at the edges. In these experiments, tolerant cells of E. coli and S. aureus were found from the 222 nm, the 405 nm, and one of the 254 nm sources. Upon examination of the power of each UV source, it was shown that the 275 nm and 254 nm sources that resulted in no tolerant cells had surface power densities [at 25 cm (10 in.)] that were more than 10-200 times greater than those that had tolerant cells. These results suggests that bacterial cells have a higher chance to develop UV tolerance under lower power UV sources (under the experimental conditions in our laboratory). Genome investigation of the tolerant colonies revealed that there are no significant differences between the cells that developed tolerance and the original organism, hinting at the need to explore the role of epigenetics mechanisms in the development of UV tolerance in these bacteria. © 2023 American Society of Civil Engineers.
ABSTRACT
Introduction: The provision of medical facilities needed for COVID-19 diagnosis is a global concern. They must be a powerful tool for detecting and diagnosing the virus quickly using a variety of tests, as well as low-cost advancements. Whereas a chest X-ray image is an effective screening technique, the image acquisition by the instruments must be read appropriately and quickly if multiple tests are performed. Objectives: COVID-19 causes continuous respiratory parenchymal ground glass and integrates respiratory opacity, with a curved shape and peripheral pulmonary dissemination in some cases, which is difficult to anticipate earlier on. In this chapter, we intend to construct a good platform to identify COVID-19 characteristics from the image of chest X-ray to aid in early analysis. Methods: In particular, based on the Cuckoo search method, this chapter provides a bioinspired CNN-based model for COVID-19 diagnosis. This method identifies different deep learning strategies of COVID-19 patients' chest X-ray images for accurate infection identification. The suggested model's performance is estimated using the Cuckoo search approach. Furthermore, the bioinspired CNN characteristics are fine-tuned using optimization algorithm. Rigorous testing reveals that suggested method may accurately categorize chest X-ray images with high performance, remembrance, and sensitivity. Results: As a result, the suggested approach can be used to classify COVID-19 diseases from chest X-ray images in real time and also accuracy will be validated. Conclusion: Finally, the investigation of comparison results illustrates the Cuckoo algorithm is realized to determine the interested regions of the COVID-19 x-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
City parks are suitable sites for the construction of emergency medical facilities. A comparison of various types of city parks revealed that country parks fit closely with site selection conditions for emergency medical facilities. Based on the latter site selection requirements, eight impact factors such as park type, effective avoidance area, spatial fragmentation degree, water source protection area, wind direction, distance from city center, impermeability, and transport duration were quantified, and then 29 country parks in the Hangzhou Urban Area were compared using Principal Component Analysis (PCA). The calculation results showed that Linglong Country Park has the highest score, taking into account the characteristics of safety, scalability, rehabilitation, convenience, pollution prevention, and isolation. Linglong can be given priority selection as a target location for emergency medical facilities. In addition, Silver Lake Country Park, Dongqiao Country Park, Taihuyuan Country Park, and Tuankou Country Park have higher scores and can be used as alternative targets for emergency plans. The scoring results prove that the evaluation method has a high degree of rigor, a significant degree of discrimination, and a high degree of consistency between the validity and weight assignment of each impact factor. In view of the different geographical conditions in each region, the weight assignment of each impact factor can be adjusted according to local conditions and can help make effective use of existing conditions and avoid disadvantages.
Subject(s)
COVID-19 , Parks, Recreational , Humans , COVID-19/epidemiology , China/epidemiology , Cities , RecreationABSTRACT
The COVID-19 is putting tremendous pressure on the medical facilities supply, as the demand for facilities has significantly outweighed the production capability. Several rogue traders have taken advantage of this issue to distribute counterfeit products. Moreover, some sellers advertise genuine products with unreasonably high prices. Our team believes that fake or overpriced facilities will significantly complicate the battle against COVID, thereby posing millions of lives to risk. That is why our team is developing V-Block. V-Block is a supply chain management system that harnesses the power of Blockchain. Its primary goals are to assist the government in tracking the product’s distribution process and help customers avoid questionable deals. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection. The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia. The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia. In this paper, we propose the methodology of identifying the cause (either due to COVID-19 or other types of infections) of pneumonia from radiology images. Furthermore, because different variants of COVID-19 lead to different patterns of pneumonia, the proposed methodology identifies pneumonia, the COVID-19 caused pneumonia, and Omicron caused pneumonia from the radiology images. To fulfill the above-mentioned tasks, we have used three Convolution Neural Networks (CNNs) at each stage of the proposed methodology. The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause, despite having a limited dataset. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
Automated COVID-19 detection based on analysis of cough recordings has been an important field of study, as efficient and accurate methods are necessary to contain the spread of the global pandemic and relieve the burden on medical facilities. While previous works presented lightweight machine learning models [9], these models may sacrifice accuracy and interpretability to integrate into mobile devices. Besides, the question of how to effectively associate indicators from audio signals to other modality inputs (i.e. patient information) is still largely unexplored, as previous works predominantly relied on simply concatenated features to learn. To tackle these issues, this paper proposes a novel Hierarchical Multi-modal Transformer (HMT) that learns more informative multi-modal representations with a cross attention module during the feature fusion procedure. Besides, the block aggregation algorithm for the HMT provides an efficient and improved solution from the Vanilla Vision Transformer for limited COVID-19 benchmark datasets. Extensive experiments show the effectiveness of our proposed model for more accurate COVID-19 detection, which yield state-of-the-art results on two public datasets, Coswara and COUGHVID. © 2022 Copyright held by the owner/author(s).
ABSTRACT
During pandemics, Intensive care units (ICUs) play a major role in providing necessary medical treatment to the patients and stabilizing dire situations. Mechanical ventilation systems are an integral part of ICUs in every medical facility. A Mechanical Ventilation system must provide accurate and fast tracking of a pre-set pressure profile. Therefore various controller designs are tested and analyzed in the presented paper for a blower-hose-patient mechanical ventilation system. The basic framework for the control problem, and necessary mathematical and simulation background is presented along with a comparative analysis of the designed control schemes. An attempt is also made to find an optimal controller design providing the desired system output with minimal trade-offs. © 2022 IEEE.
ABSTRACT
The demand for emergency medical facilities (EMFs) has witnessed an explosive growth recently due to the COVID-19 pandemic and the rapid spread of the virus. To expedite the location of EMFs and the allocation of patients to these facilities at times of disaster, a location-allocation problem (LAP) model that can help EMFs cope with major public health emergencies was proposed in this study. Given the influence of the number of COVID-19-infected persons on the demand for EMFs, a grey forecasting model was also utilized to predict the accumulative COVID-19 cases during the pandemic and to calculate the demand for EMFs. A serial-number-coded genetic algorithm (SNCGA) was proposed, and dynamic variation was used to accelerate the convergence. This algorithm was programmed using MATLAB, and the emergency medical facility LAP (EMFLAP) model was solved using the simple (standard) genetic algorithm (SGA) and SNCGA. Results show that the EMFLAP plan based on SNCGA consumes 8.34% less time than that based on SGA, and the calculation time of SNCGA is 20.25% shorter than that of SGA. Therefore, SNCGA is proven convenient for processing the model constraint conditions, for naturally describing the available solutions to a problem, for improving the complexity of algorithms, and for reducing the total time consumed by EMFLAP plans. The proposed method can guide emergency management personnel in designing an EMFLAP decision scheme.
Subject(s)
COVID-19 , Public Health , Algorithms , COVID-19/epidemiology , Emergencies , Humans , PandemicsABSTRACT
Due of the current COVID-19 pandemic crises, there is a worldwide need for quick medical findings. Furthermore, due to a lack of medical facilities and medical practitioners' hectic schedules, several examinations must now be performed by the general public. Also because of the high rate of transmissibility of COVID-19, even asymptomatic patients can readily transfer the virus to others, faster detection is critical during the initial phase of COVID-19, which is early identification. The earlier a patient is detected;the better the virus's spread may be stopped and the patient can undergo proper treatment. As the nationwide vaccination process is in its later part, it is obvious that the government will uplift its regulations and the employees will have to return to their workplaces or offices. As a solution to this upcoming urgency the authors would like to propose a solution to identify COVID-19 patients in advance at corporate level. As an IoT based solution a device is supposed to be setup on top of each employee's desk, which in return will be used to monitor the oxygen level, temperature, and heartbeat rate of the employees. © 2022 IEEE.
ABSTRACT
The Coronavirus disease 2019 (COVID-19) rapid spread across the world has unfortunately led to huge number of fatalities and large number of cases overflowing the capacity of health care facilities (HCF) causing a global public health issue. This health crisis has gotten worse with the new corona virus variant, which transmits even faster leading to critical shortage in hospital care beds and ventilators. To address this shortage, efforts are directed towards the fast construction of HCFs or conversion of non-medical facilities into temporary medical ones. However, there is a lack of structured support systems behind the decisions made. Disaster management has recently been growing in importance. It mainly addresses humanitarian logistics and emergency responses in case of disasters using various methodologies including operation research approaches. However, it did not tackle yet the emergency cases of pandemics and outbreaks. Accordingly, this paper presents the framework for a decision support system (DSS) that would help arrive at the best decision for fast provision HCFs in a timely and cost-effective manner amid health crises. The DSS consists of two modules: (1) first module determines the optimum structural system for fast construction of new HCFs considering the construction cost and duration and the associated life cycle costs, and (2) the second module determines the optimum selection of candidate non-medical facilities that can be converted into temporary HCFs considering multiple attributes. The proposed DSS will help policy makers respond quickly to pandemic crises and confine its disastrous impact on the society. © 2023, Canadian Society for Civil Engineering.
ABSTRACT
Medical facilities are an important part of urban public facilities and a vital pillar for the survival of citizens at critical times. During the rapid spread of coronavirus disease (COVID-19), Wuhan was forced into lockdown with a severe shortage of medical resources and high public tension. Adequate allocation of medical facilities is significant to stabilize citizens' emotions and ensure their living standards. This paper combines text sentiment analysis techniques with geographic information system (GIS) technology and uses a coordination degree model to evaluate the dynamic demand for medical facilities in Wuhan based on social media data and medical facility data. This study divided the epidemic into three phases: latent, outbreak and stable, from which the following findings arise: Public sentiment changed from negative to positive. Over half of the subdistricts in three phases were in a dysfunctional state, with a circular distribution of coordination levels decreasing from the city center to the outer. Thus, when facing major public health emergencies, Wuhan revealed problems of uneven distribution of medical facilities and unreasonable distribution of grades. This study aims to provide a basis and suggestions for the city to respond to major public health emergencies and optimize the allocation of urban medical facilities.
Subject(s)
COVID-19 , Social Media , Attitude , COVID-19/epidemiology , Communicable Disease Control , Emergencies , Humans , SARS-CoV-2ABSTRACT
Since the discovery of corona virus (nCOV-19), and its subsequent progression into a global pandemic, an enormous hurdle faced by hospitals and their healthcare staff has been to streamline, and look after the huge flow of cases. It has become increasingly difficult to consult a Covid specialist when the first wave occurred in rural and areas not connected as well to modern amenities. Thus, it has become obvious that an interactive Chatbot with efficient execution can help patients living in such areas by educating on the appropriate preventive measures, news on virus strains, reducing the psychological damage caused by the fear of the virus and mental effects of solitary isolation. This study displays and discusses the schematics of an artificial intelligence (AI) chatbot for the purpose of evaluation, diagnosis and recommending immediate preventive as well as safety measures for patients who have been exposed to nCOV-19, and doubles as a virtual assistant to aid in measuring the severity of the infection via symptom analysis and connects with the authorised medical facilities when it progresses to a serious stage. © 2022 IEEE.
ABSTRACT
One of the biggest health challenges that the world has faced in recent times is the pandemic due to coronavirus disease known as SARS-CoV-2, or Covid-19 as officially named by the World Health Organization (WHO). To plan medical facilities in a certain location in order to combat the disease in near future, public health policy makers expect reliable prediction of the number of Covid-19 positive cases in that location. The requirement of reliable prediction gives rise to the need for studying growth in the number of Covid-19 positive cases in the past and predicting the growth in the number in near future. In this study, the growth in the number of Covid-19 positive cases have been modelled using several machine learning based regression techniques viz., Multiple Linear Regression, Decision Tree Regression and Support Vector Regression. Further, different feature selection techniques based on Filter and Wrapper methods have been applied to select the suitable features based on which prediction is to be done. This study proposes the best observed method for modelling the pattern of growth in number of Covid-19 cases in the near future for a locality and also the best selection method that can be employed for obtaining the optimal feature set. It has been observed that unregularized Multiple Linear regression model yields promising results on the test data set, compared to the other regression models, for predicting the future number of Covid-19 cases and Backward Elimination feature selection method performs better than other feature selection methods. © 2022 IEEE.
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Coronavirus disease 2019 broke out in early 2020 and quickly spread to over 200 countries, leading to a severe health crisis for people all over the world. In high-risk areas of the epidemic, the shortage of testing reagents and medical facilities have become essential factors restricting the treatment of COVID-19 patients. Computed tomography (CT) has helped doctors make medical diagnoses in many areas as a vital technology in medical field. At present, due to personal privacy issues, it isn't easy to compare different networks because they are all conducted on different data sets, using other metrics, and can not make good use of high-resolution CT images. Based on iCTCF's public data set, 4000 photos from 61 patients are used to propose a network of high-resolution inputs for diagnosing disease using lung CT images of COVID-19 patients. Our work makes better results than traditional image classification methods in limited data sets, contributing to the advancement of deep neural networks in the field of COVID-19CT image recognition. © 2021 IEEE.
ABSTRACT
Under the growing uncertainties prevailing around the world, one of the most debated topics that have brought worldwide medical facilities to the test would be the ongoing Covid-19 pandemic devastating people from all walks of life. The constant intake of chronically ill or possible Covid19 infected patients into the hospital along with the high influx of visitors is harming the safety and wellbeing of both the front liners and patients especially when hospitals are one of the most important facilities to combat the spread of Covid-19 into the community. Concerning this, the researcher proposes a Visitation Management System (VMS) that solves certain key problems arising due to the high influx of hospital's visitors that causes a significant risk of Covid-19 infection, additional hospital staff to cope with the high influx of visitors at the entrance of the hospital, the difficulty of identifying high-risk Covid-19 infection locations in the hospital, as well as risk of shutting down hospitals if there are Covid-19 clusters found while losing profits or income. Apart from that, this research uses Rapid Application Development as the system development methodology for the development of VMS. Both requirement gathering methods via interview and questionnaire have been applied among various users. This research has included the findings of the collected analytical data to highlight the user requirements of VMS and a system providing proof of concept to underline the implementation of VMS to help curb the spread of Covid-19 in hospitals. © 2021 IEEE.
ABSTRACT
Chronic Obstructive Pulmonary Disease is the 2nd most common genesis of Non-Communicable Diseases (NCD)-related deaths in India. Not everyone had the chance to go to a medical facility or hospital for problems/diseases other than COVID-19 amidst lockdown as there was uncertainty of getting infected by COVID-19. To cater this issue this device/software can detect and diagnose diseases such as pneumonia, heart failure, chronic obstructive pulmonary disease (COPD), emphysema, asthma, bronchitis, foreign body in the lungs or airways etc. This process uses methodology of signal, sound and audio processing and image analysis. Normal sound samples of healthy human body would be taken in consideration and then be compared with the samples of the person whom it is tested on, different levels or frequency range of sounds/body noises that a person makes differs in different analysis, for example 'crackles' these are high pitched breath sounds made when the small air sacs get liquid filled and the person may have pneumonia or a heart failure. This not only work as a warning system that is early but also can reduce human workload and can deplete human error while using a stethoscope for the same. © 2021 IEEE.
ABSTRACT
Due to current pandemic situation, it is difficult for old/aged people to go out to hospitals for consulting the doctor. The people in rural areas cannot obtain professional healthcare services or emergency medical facilities due to long distance to hospital from their home, lack of doctors, hospitals and also lack of specialist doctor. The people in urban areas cannot find time to go to hospitals for their monthly checkup or for any small health issues. Hence the solution for all these issues or problems is, health care monitoring system using technology called IoT. As we are in the generation of industry 4.0, technologies such as IoT, big data, machine learning, artificial intelligence, play a vital role in our day-To-day life. As the technology is growing day by day, life is also becoming much simpler, better, faster, smart with the use of these technologies. Also by the application of these technologies, we can reduce human efforts, where by sitting at one place we can perform many tasks. Health care being a global issue, especially in India with more population, where most of who stay in rural areas are deprived of health care services. With industry 4.0 technology, we can build a IoT based device for monitoring the human vital signs, Using which, we can communicate between networked devices wirelessly, which would help the patient to get better treatment or better consultation from doctor without consulting the doctor physically. In the current situation, this system can also be effectively used for constantly monitoring covid patients requiring home isolation. © 2021 IEEE.