ABSTRACT
Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
People have long been affected by epidemics and pandemics of communicable illnesses. The outbreaks have been around for thousands of years. Even in our modern day, epidemics have ravaged civilization till it leads people to despair. In the meanwhile, viruses have always offered huge difficulties that have ignited horrific epidemics and pandemics. A pandemic is the widespread spread of a new sickness. Viral respiratory diseases, such as those caused by a novel influenza virus or the coronavirus COVID-19, are the most likely to evolve into a pandemic. A pandemic is not the same as an epidemic. In an epidemic, many more cases of a health condition occur than would typically develop in a community or area, however the ailment does not move outside. The World Health Organization (WHO) is responsible for declaring when a worldwide epidemic is underway. The WHO achieves this by monitoring outbreaks of a disease and receiving advice from worldwide health experts. This paper depicts the impact of COVID-19 in globally in various fields and also visualize the current status of this pandemic. © 2022 University of Stockholm. All rights reserved.
ABSTRACT
Aerosols emitted in densely populated and industrialized Indo-Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID-19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016-2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near-surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud-particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID-19 lockdown on regional climate and water cycle over Indo-Gangetic Plain, emphasizing need for further studies from modeling perspectives.
ABSTRACT
The novel coronavirus (COVID-19) outbreak has been announced as a “Public Health Emergency of International Concern” and further identified as a pandemic by the World Health Organization. Considering the unprecedented spread of the disease across the world, the development of noncontact and unobtrusive methods for quick screening of potential carriers is the need of the hour. The chapter describes a multimodal approach for rapid screening of potential COVID-19 carriers based on symptomatic sensing using a combination of sensors, followed by generation of a metric to indicate the severity of symptoms. The proposed system is likely to be a significant addition to the present body of systems to scan COVID-19 carriers. © 2022, Springer Nature Switzerland AG.
ABSTRACT
Background: The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS CoV-2) has the potential to affect the mental health of health-care professionals in several adverse ways, owing to a multifactorial, unprecedented volley of evolving factors. It may contribute to the elevated levels of depression, anxiety, and worry among the personnel on the frontline, who are often affected by medical and psychosocial factors, including pre-existing illness, scarcity of resources, uncertainty of outcome, prolonged working hours, lack of consensus, prevalent societal misbeliefs, prevailing stigma, and a constant threat to safety. Aim: The aim of this study was to screen health-care professionals for symptoms of depression, anxiety, and worry during SARS CoV-2 pandemic. Methods: In this cross-sectional study, a questionnaire was designed based on Patient Health Questionnaire-9, generalized anxiety disorder-7. It was disseminated through web-based and mobile-based social networks, thereby keeping in line with the social distancing and lockdown protocols. Results: Two hundred and fifty-seven participants were included in the study. Anxiety and depression were present in 23% (n = 59) and 16.8% (n = 43) of participants, respectively, most cases being mild in intensity. Nearly 56.4% (n = 145) of the participants reported that they were worried about COVID 19 pandemic, major cause of worry being risk of family and friends to be infected by COVID 19. About 40.9% (n = 105) participants reported sleep disturbances. Nearly 33.5% (n = 86) of participants reported increase in their smoking/alcohol consumption during lockdown. Conclusions: A significant proportion of health-care professionals experienced worry, sleep disturbances, anxiety, and depressive symptoms. Having clear guidelines, policies and procedures, training on infection control measures, occasional drills and being able to maintain communication with family members while in quarantine can go a long way in reducing worry and psychological impact of pandemic on health-care professionals.