ABSTRACT
COVID-19 is a worldwide pandemic that affected health care and lifestyle all over the world and early discovery is critical for controlling virus spread and mortality. The principal diagnostic test is the RT -PCR test, while test results are pending, spotting probable COVID19 infections on a chest X-ray may aid in restricting high-risk individuals if the diagnosis is done early. Most medical systems have X-Ray equipment, and since most modern X-Ray systems are automated, there is no need to transfer samples. Therefore, we recommend building a Deep Convolutional Neural Network-based technique that works on Radiography images for identifying COVID-19 positive patients. Here we have applied transfer-learning over some widely used deep CNN models like NasNet, DenseNet121, VGG19, ResNet50, and Xception. We have compared the performance of each model by running them over the COVID19 Radiography Dataset. Around 40K chest X-ray photos of COVID patients were used to build training, test, and validation sets. Since earlier research, there has been significant improvement in the number of data points to better train the CNN models. This study aims to identify the best of the available solutions that can be used by medical staff to swiftly discover COVID positive persons by just using the patient's Chest X-Ray diagnosis. © 2022 IEEE.
ABSTRACT
COVID-19 is spread mainly through respiratory droplets. With the development of COVID-19 worldwide, international airports are facing unprecedented imported risks, becoming the forefront of overseas epidemic prevention. The transmission mechanism of the disease is easy to implement due to the general human susceptibility. Despite the ongoing development of COVID-19 vaccines, the public health community still needs to establish nonpharmaceutical interventions to mitigate the spread of COVID-19 in the population, especially among individuals in close contact with confirmed cases. Since the outbreak of COVID-19, relevant authorities in China have taken active prevention and control measures, strictly tracked down and isolated those involved, and effectively contained the spread of the epidemic. Medical workers have played an important role in epidemic prevention and control. Medical workers are putting their lives and health at risk because of a lack of knowledge about COVID-19. This review summarizes the work of preventing cross-infection in the transport of high-risk groups by ambulance in primary hospitals in Jiangsu province during the COVID-19 outbreak. Through standardized management, the cross infection caused by ambulance has been effectively prevented. Therefore, during the COVID-19 outbreak, establishing a safe disinfection management system, strengthening the disinfection management of ambulance transport, and training personnel in personal protection, work requirements and emergency response skills can effectively prevent the spread of the COVID-19. © 2022 Journal of Innovation Management. All rights reserved.
ABSTRACT
In response to the pandemic caused by the rapidly spreading COVID-19 virus, several highly effective vaccines have been developed by Pfizer, Moderna, and Janssen. Despite the promising efficacy of those vaccines, there remains the challenge of properly distributing vaccines to those who need it most in the US. of particular concern are individuals who are at higher risk due to underlying medical conditions which have been shown to exacerbate COVID-19 symptoms and at times lead to fatal illnesses. In addition to this, a variety of socioeconomic factors have been linked to increased COVID-19 rates and increased mortality, such as race, age, income, mobility, and education level.This project aims to develop an information system to help advise vaccine distributors and state governments on how to effectively distribute vaccines to prioritize high risk individuals. The information system incorporates state-level data of the population with underlying medical conditions, demographics, overall state income, education level, and state mobility to formulate a mortality index. State-level data on the number of vaccines available and doses already administered are also incorporated into the information system to generate a vaccine index. The mortality and vaccine indices for each state are coupled to generate a vaccine priority ranking which can be used to advise vaccine distribution.The prototype can successfully link the data described above to a map of the US and then color code states according to the vaccine priority ranking. Implementation of this prototype will enable optimal vaccine distribution and reduce instances of severe or fatal COVID-19 illnesses as well as reduce costs associated with oversupply of vaccines in a single region. Future work will focus on improving the granularity of data down to the county-level, as well as increasing the scope of the system to the global scale. Additionally, the team plans to expand the application space of this information system to other diseases. © 2021 IEEE.