ABSTRACT
Currently, the most effective way to counteract COVID-19 is to slow its spread through personal distancing, hand washing and the use of personal protective equipment. Vaccination processes of citizens are becoming widespread. At the same time, information technology will be able to help slow down the spread of COVID-19 by early detection, prediction and monitoring of new cases. This paper provides an overview of current research on the selection and processing of COVID-19 data. The role and location of IoT devices, communication networks and cloud infrastructure for the selection and processing of COVID-19 data are described. Based on the analysis of IoT-platforms for detection and monitoring of COVID-19, the structure of the information technology platform was formed. There are included data collection tools, primary networks, Internet, cloud infrastructure, data presentation tools. The architecture of the information technology platform for the selection and processing of COVID-19 data is proposed. A description of the process of collecting and analytical processing of COVID-19 information using machine learning algorithms is given. The model of the information technology platform classes structure for the selection and processing of COVID-19 data is considered. That contains more than 50 classes to describe more than 120 characteristics of information entities. The processes of selection and aggregation of COVID-19 data and integration of analytical processing tools based on machine learning algorithms into the information technology platform are described. © 2021 IEEE.
ABSTRACT
Introduction. Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) causes a coronavirus disease 2019 (COVID-19) characterized by a "cytokine storm"-increased activity of immune cells with the elevated production of inflammatory cytokines leading to respiratory failure. Additionally, the course of pneumonia is characterized by an increased content of C reactive protein. Leukocytosis, leukopenia and lymphopenia are also commonly present in COVID-19 patients. Fine alterations of the immune cells subpopulations and cytokines level in COVID-19 patients are little known. This work aimed to determine the immunophenotype of peripheral blood cells subpopulations and to study the mentioned underlying changes in IL-6 and CRP levels in patients with COVID-19 pneumonia. Methods. Blood count with Erythrocyte Sedimentation Rate (ESR) was completed by routine blood assay. CRP and IL-6 levels were measured by ELISA. Immunophenotype of subpopulations of peripheral blood cells was acquired by multiparametric fluorescence flow cytometry. Results. Blood parameters comparison in 14 hospitalized severe COVID-19 patients showed that WBC count increased on day 7 and steadily decreased by day 28, while the percentage of neutrophils decreased gradually from the time of admission to full-recovery state. The percentage and count of lymphocytes regularly increased. The percentage of monocytes had a trend to rising but not statistically significant. The red blood cells count, ESR, and platelet count were not altered during COVID-19 progression. On initial admission, 35.7 % of COVID-19 patients had leucopenia, while 14.3 % of patients had leukocytosis on day 7. Lymphopenia occurred in 76.9 % of patients on day 0 and was not present on day 28. Changes in CRP levels were statistically significant over the duration of COVID-19 progression and recovery (p=0.003;n=15). The maximum CRP value was at day 0 but reached a normal range on day 28. The Il-6 level did not change significantly over 28 days of observation demonstrating a steady twice higher level in COVID-19 patients compared to healthy donors (p=0.934;n=8). The population of CD45+ cells was significantly lower in COVID-19 patients than in the healthy donors group (p=0.009;n=12). Despite it grew gradually from day 0 to 28, it did not reach the normal value at full-recovery state. The population of CD3+ cells was higher than the normal ranges at the first assessment, then dropped on day 7 but return to elevated levels on days 14 and 28 (p=0.008;n=12). Changes in the CD19+ cells count were significantly lower than those of the healthy volunteers during the period of observations (p=0.007;n=12). There were decreases in CD16+CD56+ cells counts over each time of assessment compared to healthy donors, but not statistically significant. Conclusion. The understanding of the dynamic changes of lymphocyte populations, cytokines production in COVID-19 patients will provide an in-depth knowledge of the COVID-19 pneumonia progression.
ABSTRACT
In December 2019, an unknown disease was first reported in Wuhan (Hubei province, China), which subsequently spread around the world. COVID-19 is an infectious disease accompanied by severe acute respiratory syndrome (SARS-CoV-2) [1], which is called a "coronavirus"for the visual similarity of the pathogen to the crown [2]. On April 23 2020, the World Health Organization has identified more than 2.5 million confirmed cases of COVID-19 virus disease [3]. Considering the scope of spread and features of the pandemic caused by the COVID-19 virus, timely diagnosis of the disease of citizens is important. The first step in developing any diagnostic or treatment tools is to effectively process data collections, much of which are based on open data from national, provincial, and municipal health facilities. At the same time, the importance of IT support the processes of making operational and strategic decisions in the health sector, as a critical area of smart cities and smart regions is increasing. Therefore, developing effective and reliable means of generating and processing detailed collections of COVID-19 outbreaks in real-time is an important research area in the implementation of innovative information technology projects of the smart city and smart region class. It requires design, development and implementation of information technology and software-algorithmic tools to collect and process data globally. © 2020 IEEE.