ABSTRACT
Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.
ABSTRACT
Since COVID-19 appeared in December 2019, scientists are researching new ways to improve the management of the disease. Considering machine learning approaches have proven to be very useful tools to discover hidden patterns in data, we propose in this paper to apply a Self Organizing Map (SOM) to characterize the health-status evolution of COVID-19 patients. The SOM is a neural network whose neurons can be represented as cells in a bi-dimensional grid preserving the mapping from the original space to the map units. We consider real-world data of hospitalized COVID-19 patients in a Spanish hospital during the first wave of the pandemic. Patients are represented by six blood tests (leukocytes and D-dimer, among others) in a daily basis. Besides, each patient is associated with one of two different health-status: favorable evolution (discharged home) and unfavorable evolution (exitus or admission to the intensive care unit). We show the potential of our approach by detailing the mapping of the health trajectory associated with different particular cases and drawing their trajectory on the bi-dimensional map of the SOM. © 2021 IEEE.