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Journal of Clinical and Diagnostic Research ; 16(8):DC33-DC38, 2022.
Article in English | EMBASE | ID: covidwho-2033411


Introduction: It is crucial to determine possible factors associated with exacerbation of the disease due to the alarming global spread, morbidity and mortality associated with Coronavirus Disease-2019 (COVID-19). It is important to determine the co-morbidities associated with this disease which will help in better treatment of patients in time and to make amendments to management policy. Aim: To compare the clinical features, and predisposing factors (socio-demographic factors and co-morbidities) influencing the outcome in COVID-19 infected patients admitted in a tertiary care centre in the first and second wave of COVID-19 pandemic. Materials and Methods: The retrospective study was conducted at the Department of Microbiology, Dr. Shankarrao Chavan Government Medical College, Nanded, Maharashtra, India. The data was collected from the electronic resource which was maintained by the institute Integrated Disease Surveillance Program (IDSP) health record reporting database for the duration of June 2020 to August 2021. This data included patient’s demographic details (age, sex, address, contact number), other details (history of close contacts, international travel) clinical history, different types of symptoms (ICMR patient category), co-morbidities, number of patients requiring ICU admission, type of sample, the outcome in terms of death and discharge, cause of death. The analysis was done for the complete data and then for two separate durations of the first and second wave which were compared later with Chi-square test (Bivariate analysis). Results: A total of 8841 patients were involved and the majority of patients in the study were between the age group of 30-75 years, there was a predominance of males in first and second waves with 6514 (73.7%) and 5795 (58.6%) respectively. The paediatric patients had a mortality rate of 100% (n=7) found in the second wave. Fever (39%) and dyspnea (22%) were found as the commonest presentation in both waves. Gastrointestinal manifestations were observed relatively more in the second wave. The serious patients on ventilator were found to have (>91%) the highest mortality. It appeared that the highest attributable risk to severity and mortality (eight to ten times increased) was due to hypertension, diabetes and other co-morbidities. Pregnancy did not predisposed to be as a risk factor. Conclusion: Prompt management and preventive care are needed for patients with co-morbidities to avoid the exacerbation of COVID-19 as well as drug cross interactions.

NeuroQuantology ; 20(7):2691-2701, 2022.
Article in English | EMBASE | ID: covidwho-1969834


Human face detection is a computer vision application. Face image processing has been the subject of various studies. Several researchers have previously investigated facial recognition. We used IOT and AI algorithms with the basic notion of human face identification in this research to identify the covid-19 patient travelling in public locations during isolation period.-19 criteria for Human face discovery is the novel notion in this covid. An Internet of Things (IoT) method is used to store daily averages of 19 positive cases across districts. The information that can be stored, such as a person's name, phone number, and address (with different poses). Personal information is securely saved in the cloud database and can be accessed at any time by logging into your account. IoT and Raspberry Pi are used to store and retrieve data. Face detection technology in CCTV cameras is used to keep tabs on the current scenario and identify any people who might be in the video. We installed cameras in strategic locations and linked them to the cloud server so that the faces of those with and those without covid 19 could be forwarded. hange detection methodologies in remotely sensed images suffer from the problem of data inadequacy;and to handle this problem, semi-supervised approaches can be opted. Semi-supervised Modified Self-organizing Feature Map is used to classify covid positive and normal cases in this recognition method. Every time a person's face is taken by the camera and compared to a database, an AI algorithm is used to identify and categorise the person (testing centre data). Covid positive patients will be flagged by an AI system, and their personal data will be sent to a government health care unit, which may take legal action against them, in this classification process. OpenCV and the Python platform were used to carry out this experiment. Public exposure to covid 19 will be reduced, and mortality rates owing to covid illness will be reduced as a result of this proposed model.