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1.
Current Research in Biotechnology ; 4:564-578, 2022.
Article in English | EMBASE | ID: covidwho-2177931

ABSTRACT

Electrochemical biosensors are analytical devices that hold a current across the surface of an electrode on which biological receptors are immobilized. These devices enable the conversion of physio-biochemical reactions by biological molecules into electron movements, so the output can be observed as the flow of charge across the electrode. These biosensing platforms detect changes in the reactive and resistive properties of the electrode surface when an alternating current (AC) or voltage is applied to output signals. Impedance-based electrochemical biosensors have advantages compared with other biosensors, such as high sensitivity, low cost, and ease of operation. In addition to uses as miniature detection tools, biosensors and microfluidics play vital roles in nano-diagnostics. Many sensors have been developed at the nanoscale by exploiting the greater conductivity across the electrodes and improved specificity for biorecognition element-receptor binding in biosensing devices. Several of these sensors have been assessed in trials and emerged as clinical products for detecting and diagnosing diseases, bacteria, viruses, deficiencies, and biofluid malfunctions in the human body. This review summarizes advances in impedance-based biosensors and their working principles and classifications, as well as providing relevant illustrations by focusing on the essential biorecognition elements, receptors, and target molecules during diagnosis. Copyright © 2022 The Authors

2.
Environmental Monitoring & Assessment ; 195(1):223, 2022.
Article in English | MEDLINE | ID: covidwho-2174544

ABSTRACT

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

3.
Szociologiai Szemle ; 32(4):70-91, 2022.
Article in Hungarian | Scopus | ID: covidwho-2206949

ABSTRACT

In this paper, we provide an empirical, descriptive analysis of the social networks of Hungarian society and illustrate how the network scale-up method estimates the size of hard-to-reach subpopulations and segregation of social groups. Based on a representative survey of 7000 respondents from Hungary (HS2021), we first estimate the average size of the respondents' personal networks. Then, we examine the social fault lines along various social groups and how accurately the network scale-up method estimates the size of these groups (e.g., unemployed, protesters, the Roma, Covid-infected). These estimates are then compared with data from other sources (census data, administrative data, surveys). Our results show that the network scale-up method estimates the size of visible social groups (e.g., the Roma, homeless people) quite well. The visibility of other social groups appears to be much lower. Social fault lines are greatest in the case of homeless people, protesters, and members of NGOs. Finally, we describe recent methodological advancements and summarize our suggestions for future research using this method. © 2022, Hungarian Sociological Association. All rights reserved.

4.
Pediatric Diabetes ; 23(Supplement 31):52-53, 2022.
Article in English | EMBASE | ID: covidwho-2137186

ABSTRACT

Introduction: According to WHO and IDF it is stated that healthy diet and regular physical activity and maintaining healthy weight is very effective for type 1 and type 2 diabetes. Objective(s): The main objectives of study was to schedule personalized healthy nutrition, to programmed physical training schedule & was to find out the impact of nutrition & physical activity in term of SMBG changes, weight, muscle mass, Hba1c. Method(s): Number of type 1 diabetic children & adolescents enrolled for the intervention were 15 (F = 9, M = 6). Number of subject completed the intervention was 13. The intervention conducted for 3 months during the lockdown from 1 April to 1 July 2020. All subjects were counseled and educated and followed up Through teleconsultation at baseline visit (Day 0), visit 1 on (day 15), visit -2 on (day 30) visit 3 on (day 60) visit 4 on (day 90). The parameters evaluated were anthropometric data,HBA1C & SMBG readings from baseline to end of the intervention. Result(s): Data showed significant improvement of hba1c of 0.8%, also improvement of glycemic control is seen. On evaluation of anthropometric data there was no significant changes in weight but increment seen in muscle mass. Conclusion(s): It has been concluded that MNT & physical activity comprise the basic pillars in treatment of diabetes. Especially for kids, young adults with a chronic condition following a strategic plan for nutrition & exercise can do in appropriate growth and development. In the real world, implementation of the MNT and exercise for the pediatric population still remains a challenge. This can be easily solved by including sports of the person's choice in their routine activities and healthy food options. These together can impact the glycemic status and the quality of life of young adults up to great extent.

5.
Journal of Data, Information and Management ; : 1-24, 2022.
Article in English | PubMed Central | ID: covidwho-2119535

ABSTRACT

Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve the changepoint detection problem using the fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, the asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under the null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in the estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, a high degree of fuzziness in true changepoints, and also to hyperparameter values. Simulation studies reveal that the proposed method beats other methods of gradual changepoint detection (including MJPD, HSMUCE, fuzzy methods like FCP, FCMLCP etc) and also popular crisp methods like Binary Segmentation, PELT, and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19. We have developed the python package roufcp for broader dissemination of the methods.

6.
3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022 ; 491:673-685, 2023.
Article in English | Scopus | ID: covidwho-2094556

ABSTRACT

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
Sci Total Environ ; 2022.
Article in English | PubMed Central | ID: covidwho-2061859

ABSTRACT

The COVID-19 era has profoundly affected everyday human life, the environment, and freshwater ecosystems worldwide. Despite the numerous influences, a strict COVID-19 lockdown might improve the surface water quality and thus provide an unprecedented opportunity to restore the degraded freshwater resource. Therefore, we intend to investigate the spatiotemporal water quality, sources, and preliminary health risks of heavy metal(loid)s in the Karatoya River basin (KRB), a tropical urban river in Bangladesh. Seventy water samples were collected from 35 stations in KRB in 2019 and 2022 during the dry season. The results showed that the concentrations of Ni, Cu, Zn, Pb, Cd, and Cr were significantly reduced by 89.3–99.7 % during the post-lockdown period (p < 0.05). However, pH, Fe, Mn, and As concentrations increased due to the rise of urban waste and the usage of disinfectants during the post-lockdown phase. In the post-lockdown phase, the heavy metal pollution index, heavy metal evaluation index, and Nemerow's pollution index values lessened by 8.58 %, 42.86 %, and 22.86 %, respectively. Besides, the irrigation water quality indices also improved by 59 %–62 %. The total hazard index values increased by 24 % (children) and 22 % (adults) due to the rise in Mn and As concentrations during the lockdown. In comparison, total carcinogenic risk values were reduced by 54 % (children) and 53 % (adults) in the post-lockdown. We found no significant changes in river flow, rainfall, or land cover near the river from the pre to post-lockdown phase. The results of semivariogram models have demonstrated that most attributes have weak spatial dependence, indicating restricted industrial and agricultural effluents during the lockdown, significantly improving river water quality. Our study confirms that the lockdown provides a unique opportunity for the remarkable improvement of degraded freshwater resources. Long-term management policies and regular monitoring should reduce river pollution and clean surface water.

8.
Indian J Endocrinol Metab ; 26(4): 376-383, 2022.
Article in English | MEDLINE | ID: covidwho-2055694

ABSTRACT

Background and Objectives: Diabetes mellitus is associated with poor clinical outcomes in patients with coronavirus disease 2019 (COVID-19). This study aimed to explore the clinical characteristics of patients with type 2 diabetes with COVID-19, and to determine the impact of type 2 diabetes on clinical outcome of patients with COVID-19. Material and Methods: This single-center, retrospective, observational study enrolled patients admitted from March 2020 to June 2021 with COVID-19. The clinical and biochemical characteristics of patients with known type 2 diabetes, newly diagnosed diabetes, type 2 diabetes with comorbidities and those who succumbed to illness were analyzed. Results: Of 4,559 patients with COVID-19, 2,090 (45.8%) had type 2 diabetes. Patients with COVID-19 with diabetes were older, more likely to receive mechanical ventilation, had higher odds of mortality from COVID-19 as compared with patients without diabetes. In addition, patients with diabetes had significantly higher levels of serum creatinine, C-reactive protein, ferritin, lactate dehydrogenase, and D-dimer. Compared with previously diagnosed patients with diabetes, newly diagnosed patients had higher mortality (33% vs. 27%, P = 0.049). Among patients with COVID-19 and diabetes, nonsurvivors had significantly higher levels of inflammatory markers and had severe impairment of cardiac, renal, and coagulation parameters as opposed to survivors. Conclusion: Patients with COVID-19 with diabetes were more likely to have severe disease and had higher mortality. Presence of chronic kidney disease and coronary artery disease in patients with diabetes with COVID-19 was associated with adverse outcome. Patients with newly diagnosed diabetes had higher odds of severe disease at presentation and had higher mortality.

9.
Indian Journal of Endocrinology and Metabolism ; 26(4):376-383, 2022.
Article in English | EuropePMC | ID: covidwho-2046215

ABSTRACT

Background and Objectives: Diabetes mellitus is associated with poor clinical outcomes in patients with coronavirus disease 2019 (COVID-19). This study aimed to explore the clinical characteristics of patients with type 2 diabetes with COVID-19, and to determine the impact of type 2 diabetes on clinical outcome of patients with COVID-19. Material and Methods: This single-center, retrospective, observational study enrolled patients admitted from March 2020 to June 2021 with COVID-19. The clinical and biochemical characteristics of patients with known type 2 diabetes, newly diagnosed diabetes, type 2 diabetes with comorbidities and those who succumbed to illness were analyzed. Results: Of 4,559 patients with COVID-19, 2,090 (45.8%) had type 2 diabetes. Patients with COVID-19 with diabetes were older, more likely to receive mechanical ventilation, had higher odds of mortality from COVID-19 as compared with patients without diabetes. In addition, patients with diabetes had significantly higher levels of serum creatinine, C-reactive protein, ferritin, lactate dehydrogenase, and D-dimer. Compared with previously diagnosed patients with diabetes, newly diagnosed patients had higher mortality (33% vs. 27%, P = 0.049). Among patients with COVID-19 and diabetes, nonsurvivors had significantly higher levels of inflammatory markers and had severe impairment of cardiac, renal, and coagulation parameters as opposed to survivors. Conclusion: Patients with COVID-19 with diabetes were more likely to have severe disease and had higher mortality. Presence of chronic kidney disease and coronary artery disease in patients with diabetes with COVID-19 was associated with adverse outcome. Patients with newly diagnosed diabetes had higher odds of severe disease at presentation and had higher mortality.

10.
International Journal of Applied Pharmaceutics ; 14(5):22-31, 2022.
Article in English | EMBASE | ID: covidwho-2044320

ABSTRACT

A novel coronavirus disease, which is transmitted from human to human has quickly become the cause of the current worldwide health crisis. This virus is, also known as SARS coronavirus, belongs to the Coronaviridae family of viruses. The recent outbreak of acute respiratory disorders starting in Wuhan, China is found to be caused by this virus. The condition caused by it, known as COVID-19 has spread very rapidly all over the world, causing so many death. This led WHO on Mar 11, 2020, to designate it as a global pandemic. An update on the history, etiology, epidemiology, pathophysiology and preventive methods for COVID-19 such as masking, quarantine, and social distancing are discussed in this paper. Repurposed drugs, antibodies, corticosteroids, vaccination and plasma transfusion, are among the treatments explained in the study. Finally, the study discusses India’s COVID vaccination programme. The major aspects of this entire review are to describe COVID-19 infection, its prevention and treatment approach.

11.
NeuroQuantology ; 20(9):1989-2008, 2022.
Article in English | EMBASE | ID: covidwho-2044242

ABSTRACT

Background and Purpose: The COVID-19 epidemics are causing the main rash in more than 151 countries around the whole world.Covid-19 has a bad effect on human life worldwide. One of the critical steps in fighting COVID-19 is finding the contaminated patients early enough and putting these infected people under special care. Our main aim is to separate COVID-19 patients from other patients. Materials and Methods: In this research article, we used GoogleNet as a learning network. GoogleNet is a deep convolutional neural network of 22 layers deep. We have used a pre-trained version of the GoogleNet trained on ImageNet. The pre-trained GoogleNet image input size is 224 x 224.GoogleNet;the deep convolutional neural network model can analyze X-ray images to classify the patient’s condition of the affected disease. Result: Experiments and evaluation of the GoogleNet have been effectively done based on 80% of X-ray pictures for training and 20% of X-ray pictures for testing phases respectively. GoogleNet shows a good result for disease classification with 91.40% of accuracy in 2.49 minutes. Conclusion: In this research paper, we have used the deep CNN model to classify COVID-19 disease using X-ray images based on the projected GoogleNet. Scientific studies will be the next goal of this research article.

12.
Journal of family medicine and primary care ; 11(6):3100-3103, 2022.
Article in English | EuropePMC | ID: covidwho-2034335

ABSTRACT

Background: SARSCoV-2, a coronavirus that causes COVID-19, is spreading rapidly. By the middle of August-2021, it has affected over 3 million confirmed cases in India. The main aim of this study was to examine the clinical profile of COVID-19 patients and their length of stay during treatment in a hospital. Materials and Methods: It was a hospital-based retrospective study conducted by using a total enumeration technique in July–August 2021 at Nehru Hospital, Postgraduate Institute of Medical Education and Research (PGIMER) in India. The present study was conducted on 72 COVID-19 patients who took treatment in 4C and 5C wards. Structured questionnaires were used to collect data, which included bio-demographic factors and questions about their treatment and length of stay. Results: The majority of the 72 COVID-19 positive patients were men (62%), belonged to the age group of 41–60 years (35%), had SpO2 levels ranging from 91%–95% (45%), and received room air O2 therapy (63%) during their treatment in the hospital. Female patients had a longer length of stay (7.33 days), patients under the age of 20 years had the longest hospital stay (11.5 days), patients with SpO2 less than 70% had the longest hospital stay (8 days), and patients who received oxygen using a non-rebreathing mask had the longest hospital stay (11 days). Conclusion: To avoid panic situations, regular admission and discharge of patients was essential due to the considerable increase in cases during the second wave. Patient length of stay was reduced as a consequence of collaboration and cooperation among all physicians, residents, staff nurses, and paramedics, with the goal of discharging the patient after a room air trial and follow up if needed.

13.
6th International Conference on Advances in Computing and Data Sciences, ICACDS 2022 ; 1614 CCIS:112-123, 2022.
Article in English | Scopus | ID: covidwho-2013955

ABSTRACT

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

14.
6th International Conference on Advances in Computing and Data Sciences, ICACDS 2022 ; 1613 CCIS:107-120, 2022.
Article in English | Scopus | ID: covidwho-2013950

ABSTRACT

A healthcare provider’s ability to quickly and efficiently process claims and quantify denial rates is critical to ensure smooth revenue cycle management and medical reimbursement. But the hospitals and medical practitioners are receiving more claim denials from payers, with the average rate of denial steadily increasing year over year. The recent COVID-19 pandemic has further accelerated the denial rate. An accurate denial detection algorithm can help to reduce the burden on healthcare providers. In this study, we propose a boosting-based machine learning framework to predict the likelihood of claims being denied along with the reason code at a line level. Prediction at a line level provides a finer-grained explanation to the administrative staff by pointing out the specific line for corrections. The list of important features provides an interpretable solution to the healthcare providers which enables them to create the right edits and correct the claim before going out to the payer. This in turn helps the healthcare provider dramatically improve both net patient revenue and cash flow. They can also put a check on their costs, as fewer denials mean less rework, resources, and time devoted to appealing and recovering denied claims. The denial model showed good performance with Area Under the Curve (AUC) of 0.80 and 0.82 for professional and institutional claims respectively. According to our estimates, the model has the potential to save 15%–50% of the denial cost for a healthcare provider. This in turn would have a tremendous impact on the healthcare costs as well as help make the healthcare process smoother. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

15.
NeuroQuantology ; 20(8):5821-5834, 2022.
Article in English | EMBASE | ID: covidwho-2010514

ABSTRACT

Public health and societal efforts can avoid the 2019 Corona pandemic (COVID-19). Ethiopia has adopted health and social measures. COVID-19 social distance and health prevention research. SARS-CoV-2 produces COVID-19. The global vaccine effort must understand how the virus spreads to end the pandemic. SARS-CoV-2 spreads by respiratory droplets and aerosols, according to new studies. Temperature, humidity, precipitation, air currents, pH, and radiation affect transmission. Hand washing and masks are also helpful public health measures. Non-pharmaceutical remedies need more research. Body-invading eye bacteria exist. There's no indication that COVID-19 exposure causes the disorder's ocular symptoms. Tears and conjunctiva contained SARS-CoV-2. Ocular symptoms may be the first or only sign of infection. Hand cleanliness, social isolation, and hospital SOPs can limit illness spread. Eye lubricants and spectacles can prevent eye infections.

16.
Journal of Neurology Neurosurgery and Psychiatry ; 93(9), 2022.
Article in English | Web of Science | ID: covidwho-2005428
17.
3rd IEEE Conference on VLSI Device, Circuit and System, VLSI DCS 2022 ; : 269-274, 2022.
Article in English | Scopus | ID: covidwho-1985511

ABSTRACT

With the development of mankind and growth of our civilization, more and more need is felt for exploration of and working in hazardous environments. The fast development of technology and capability to perform complex computing has made it possible for mankind to remotely perform some work which would otherwise require manual working in close proximity of hazardous environment. Thus mankind has been able to develop remotely controlled vehicle to explore the hazardous environment. A semi-autonomous Remotely Operated Vehicle (ROV) is developed in the present work. A webcam and sensors mounted on the ROV transmits the picture of the immediate neighbourhood and data collected by sensors, to the computer stationed in a remote control room using a Arduino and a few other modules. The picture and sensor data are presented to the operator by a Man Machine Interface. The operator remotely controls the movement of the ROV using a Joystick interfaced to the computer. The ROV has been successfully tested in an infectious disease ward ( like covid ward) of a hospital to deliver medicine, food, clothes and goods to the patients who are suffering from infectious disease by a health care person controlling the ROV from a remote control room. © 2022 IEEE.

18.
Us Pharmacist ; 47(4):14-14, 2022.
Article in English | Web of Science | ID: covidwho-1976210
19.
4th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2022 ; 480 LNNS:77-89, 2022.
Article in English | Scopus | ID: covidwho-1958945

ABSTRACT

The Covid-19 pandemic has had a profound effect on our daily lives. One of the most effective ways to protect ourselves from this virus is to wear face masks. This research paper introduces face mask detection that authorities can use to reduce and prevent COVID-19. The face mask recognition process in this research paper is done with a deep learning algorithm and image processing done using MobileNetV2. Steps to build the model are data collection, pre-processing, data classification, model training and model testing. The authors came up with this approach due to the recent Covid-19 situations for following specific guidelines and the uprising trend of Artificial Intelligence and Machine Learning and its real-world practices. This system has been made to detect more than one person whether they are wearing masks or not. This system also gives us the Covid cases-related worldwide updates as per our chosen country and type of cases like total cases, total deaths etc. Such systems are already available, but the efficiency of the available mask detection systems was not achieved thoroughly. This newly developed system proposes to take a step further, which recognizes more than one person at a time and increases the accuracy level to a much greater extent. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

20.
J Family Med Prim Care ; 11(6): 3100-3103, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1934401

ABSTRACT

Background: SARSCoV-2, a coronavirus that causes COVID-19, is spreading rapidly. By the middle of August-2021, it has affected over 3 million confirmed cases in India. The main aim of this study was to examine the clinical profile of COVID-19 patients and their length of stay during treatment in a hospital. Materials and Methods: It was a hospital-based retrospective study conducted by using a total enumeration technique in July-August 2021 at Nehru Hospital, Postgraduate Institute of Medical Education and Research (PGIMER) in India. The present study was conducted on 72 COVID-19 patients who took treatment in 4C and 5C wards. Structured questionnaires were used to collect data, which included bio-demographic factors and questions about their treatment and length of stay. Results: The majority of the 72 COVID-19 positive patients were men (62%), belonged to the age group of 41-60 years (35%), had SpO2 levels ranging from 91%-95% (45%), and received room air O2 therapy (63%) during their treatment in the hospital. Female patients had a longer length of stay (7.33 days), patients under the age of 20 years had the longest hospital stay (11.5 days), patients with SpO2 less than 70% had the longest hospital stay (8 days), and patients who received oxygen using a non-rebreathing mask had the longest hospital stay (11 days). Conclusion: To avoid panic situations, regular admission and discharge of patients was essential due to the considerable increase in cases during the second wave. Patient length of stay was reduced as a consequence of collaboration and cooperation among all physicians, residents, staff nurses, and paramedics, with the goal of discharging the patient after a room air trial and follow up if needed.

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