CLINICAL MEMORY OF COVID-19 PANDEMIC: A COMPARATIVE ANALYSIS OF CLINICAL AND ANGIOGRAPHIC CHARACTERISTICS OF PATIENTS WITH ACUTE CORONARY SYNDROME

Objectives: To describe the clinical characteristics and angiographic features of COVID-19 patients presenting with acute coronary syndrome (ACS) and to compare with non-COVID-19 ACS patients presenting simultaneously. Methodology: In a case control design, data were extracted from a prospectively collected COVID-19 and NCDR registry. All ACS patients who underwent cardiac catheterization from April 2020 to May 2021 were included. All of the patients were taken to the Cath lab for diagnostic coronary angiography and possible percutaneous intervention. Demographic and clinical characteristics, angiographic features, and in-hospital outcomes were compared between ACS patients with and without COVID-19. Results: A total of 4027 COVID-19 negative patients, and 80 COVID-19 positive were included. Total of 83% in COVID-19 and 88% in non-COVID-19 group had ST elevation myocardial infarction. Majority of the COVID-19 positive patients had sub-optimal TIMI flow grade (<III) post procedure and had a high thrombus burden (11.2% vs. 2.9%;p<0.001). Majority of the patients who had COVID-19 and ACS required mechanical circulatory support (48.8% vs. 0.3%;p<0.001). The mortality rates were also higher in COVID-19 positive group (38.8% vs. 1.3%;p<0.001). Among the COVID-19 positive patients 66.3% (53) had high thrombus burden (≥4 grade), intervention was performed in 73.7% (59). Post-intervention myocardial blush grade ≤2 was observed in 57.6% (34), slow flow in 85.3% (29), and phasic flow possibly due to elevated LVEDP in 41.2% (14) patients. Conclusion: COVID-19 patients with ACS had a higher severity of illness at presentation and worse outcomes as compared to simultaneously presenting non-COVID patients. © 2023 The authors.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

COVID-19 Pandemic and the Challenges for Urban Informal Sector: An Insight from Street Vendors of the District Srinagar, Kashmir

This paper aims at presenting the findings of the study on the position of street vendors in the District Srinagar, Kashmir, in India, including both men and women, during the COVID-19 lockdown. The purpose of the study was to explore various challenges street vendors faced during the COVID-19 lockdown and to highlight the vulnerability of this particular group of informal workers. The data was collected through face-to-face interviews with the use of a questionnaire, on a sample of 150 street vendors from the District Srinagar. In addition, a certain number of in-depth interviews with selected respondents from the sample was done. The study findings show that the majority of the respondents have lost their job during the peak period of COVID-19, i.e. from March to July 2020. The findings have also revealed that the lockdown directly impacted the socio-economic conditions of the workers which made it very difficult for them to survive during the peak of COVID-19. Additionally, workers were struggling very hard in order to fulfill the basic daily needs of their families. Therefore, it is suggested that the government of India should provide financial support to street vendors in order to compensate for the loss caused due to the COVID-19 lockdown.

Effectiveness of Non-Invasive Ventilation with Awake Prone in Covid-19 Related-Acute Respiratory Distress Syndrome at Covid-19 Isolation Hospital Muzaffarabad

Background: Despite its widespread usage, invasive positive pressure ventilation (IPPV) has a dismal track record in COVID-19 patients with SARDS. Currently, there is a paucity of evidence supporting the usefulness of noninvasive positive pressure ventilation (NIPPV) in the treatment of severe ARDS, as well as a significant risk of aerosol formation in patients with COVID-19 infection. Objective(s): This study aims to assess the efficacy and safety of NIPPV administration to COVID-19 patients. Method(s): The trial included 130 participants with moderate tosevere ARDS based to the Berlin criteria (PaO2/FiO2 ratio of 200mmHg, GCS > 13, respiratory breathing index (RBI) of 105, and no systemic issues). They were treated with NIPPV with awake proaning up to 12 hours per day at a hospital in Muzaffarabad. The addition of a heat and moisture exchanger (HME) and viral/bacterial filters to the expiratory limb of the ventilator circuit represented a minor improvement. Result(s): In an average of six days, the PaO2/FiO2 ratio indicates that the severity of ARDS has improved from moderate/severe to mild in 64 percent of patients. 36 percent of individuals who had a defined airway experienced IPPV or failure of NIV. During the study period, 1 % the of healthcare workers (HCW) were infected with COVID19. The delivery of NIPPV was associated with claustrophobia, nasal crusting, aspiration, and barotrauma (0.7 percent). Conclusion(s): In selected patients, NIV with awake proaning up to 12 hours per day can be employed to give respiratory support without the need for IPPV, hence eliminating the need for IPPV in those patients. However, larger-scale investigations are required to validate our findings. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Sentiment Analysis of Nationwide Lockdown amid COVID 19: Evidence from Pakistan

A growing number of people are using tweets about the recent coronavirus epidemic of COVID-19 as a dataset to determine how worried people are in different parts of the world. This study attempts to uncover the key sentiments expressed by Twitter users regarding the COVID-19 epidemic by categorizing the tweets into positive and negative sentiments utilizing several resources (such as the Twitter search application programming interface (API), the Tweepy Python library, and the CSV excel database), as well as some predefined search terms ('#LockdownPakistan.'). We extracted the text of English language tweets from 28th March-1st May 2020. We have performed the sentiment analysis and classified the tweets in a binary class of positive and negative. Further, we used the word frequencies of single (unigrams), double (bigrams), and three words to examine the gathered tweets (tri-grams). According to our data, the majority of tweets express a positive attitude, with the word for lockdown COVID-19 appearing frequently. When looking at frequency analysis, the word 'family and time' stood out among the other words, which suggests that tweets were mostly optimistic and sentiments of defeating SARS-COV-2 prevail. People are determined to spend the lockdown in a good way. However, a few of the negative tweets, nevertheless, should serve as a warning for healthcare officials to make appropriate arrangements. Public health crisis responses are today complicated and highly synchronized both offline and online. Social media is a significant medium that gives people the chance to communicate with healthcare authorities directly. © 2022 IEEE.

Antidepressant and Antipsychotic Prescribing Following Icu Admission Due to Covid-19

INTRODUCTION: Patients with post-intensive care syndrome (PICS) suffer from a constellation of cognitive, mental, and physical effects. Previous studies have demonstrated increased antidepressant prescribing in patients with PICS. The purpose of this study was to examine the rates of antidepressant and antipsychotic use in patients with PICS following critical illness due to COVID-19. METHOD(S): This was a retrospective chart review of adult patients (>18 years old) with PICS following critical illness due to COVID-19 who had at least 1 visit to the ICU survivor clinic at IU Health between September 2020 and July 2022. Patients were excluded if they were never admitted to the ICU, were admitted to a non IU Health ICU, or if they had incomplete medical records. The primary endpoint was the rate of combined antidepressant and antipsychotic use before ICU admission compared to their last clinic visit. The chi-square test was used to compare nominal data. RESULT(S): A total of 117 patients presented to the survivor clinic during the study time period. 35 patients were included in the analysis. Patients had a mean (+/- SD) age 53 (+/- 17) years and tended to be Caucasian 23 (66%) with low rates of having a college degree 6 (17%). During their ICU stays patients there were high rates of mechanical ventilation 24 (69%). While use of ECMO 9 (26%) and documented delirium 10 (28%) were less frequent. Underlying conditions identified at the initial clinic visit included cognitive impairment 20 (57%), depression 11 (31%), anxiety 8 (23%), and PTSD 4 (11%). Antidepressant and antipsychotic use was higher at the last clinic visit when compared to prior to ICU admission [16 (46%) vs. 5 (14%);p=0.0041]. At the time of the last clinic visit none of the 16 patients were receiving an antipsychotic while all were receiving an antidepressant. CONCLUSION(S): Patients with PICS following critical illness due to COVID-19 have an increased rate of antidepressant prescribing use compared to pre-illness. Further research is needed regarding the management and outcomes of these patients following critical illness due to COVID-19.

Other impacts of COVID: observations from the frontline

P77 Table 1Point prevalence (in the months of Feb & Sept) 2018 – 2022Point prevalence (Feb & Sept) 2018 2019 2020 2021 2022(only Feb 2022) NSCLC 13 12 10 21 7 SCLC 3 5 1 0 3 Mesothelioma 1 3 3 1 0 Stage 1 27% 24% 29% 31% 28% Stage 2 4% 12% 0% 3% 6% Stage 3 14% 24% 06% 16% 17% Stage 4 55% 40% 65% 50% 50% M1a 7(35%) 9(45%) 4(28.5%) 5(22.7%) 2(20%) M1b 3(17.6%) 0 (0%) 2(14.25%) 1(4.5%) 3(30%) M1c 2(11.7%) 1 (5%) 5 (35.7%) 10 (45.45%) 4(40%) Metastasis Brain 2 (11.7%) 1 (5%) 2 (14.2%) 5 (23%) 1 (10%) Adrenal 4 (24%) 2 (10%) 1 (7%) 5 (23%) 1 (10%) Bone 7 (41) 0 (0%) 3 (21%) 8 (36%) 7 (70%) Liver 6 (35%) 3 (15%) 1 (7%) 7 (32%) 5 (50%) Total 12 (71%) 10 (50%) 11 (79%) 16 (73%) 7 (70%) ConclusionThere was a drop in new lung cancer diagnoses and histological confirmation during the pandemic, followed by a trend of increased in numbers and more advanced stage at time of diagnoses in 2022 and statistical significant increase in patients with M1c.While these findings are from a single centre, they highlight a pressing need to prioritise the analysis and publication of national trends. Understanding these trends and their causes – such as delays in presentation or referrals from primary care, and reduced access to diagnostic procedures – will be key to mitigating morbidity and mortality in Lung cancer in the UK.

Acceptance and Perception of Covid-19 Vaccination for Children

Covid-19 vaccine hesitancy and acceptance delay is an unprecedented challenge for concerned authorities. Existing studies lack the investigation about public vaccination acceptance, specifically for children. In this study, we surveyed the adult population in the UK to determine the diversity in public perception and acceptance of Covid-19 vaccination specifically for the children, among different sociodemographic groups. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity and vaccination acceptance for children and their families. Acceptability for children is significantly dependent on ethnicity (p=3.7e-05), age group, and gender, where only 47% of participants show willingness towards children's vaccination. Primary dataset in this study, along with the experimental outcomes, might be useful for public awareness and policy makers towards better preparation for future epidemics as well as working globally to combat the ongoing Covid-19 variations while running effective vaccination campaigns in the identified sociodemographic groups. © 2022 IEEE.

Investigating Distorted Thinking Patterns and Psychological Distress in Students taking Online Education during COVID-19 Outbreak

Purpose: The objective of this study was to assess the predictive association between distorted thinking patterns and psychological distress (depression, stress, anxiety) in university e-learners during COVID-19 outbreak. Methodology: In this correlational study, 643 participants between age18 to 29 years (M= 21.27, SD+4.06) participated online through convenient sampling technique. They were sent an online google questionnaire, including the informed consent form, the depression, anxiety, stress scale (DASS-21), and cognitive distortions scale in Urdu, which assessed the distorted thinking patterns of adults. Findings: Analysis through Pearson product moment correlation revealed that the distorted thinking patterns of predictive thinking, rigid thinking and stress-creating thinking pattern had a strong positive association with depression, stress, and anxiety. The distorted thinking pattern of self-criticism/selfblame also had a strong positive association with depression and stress, and a moderate positive association with anxiety. Multiple stepwise regression was performed to calculate the predictive association between distorted thinking patterns and psychological distress of university students seeking digital education during the COVID-19 outbreak. Analysis revealed that distorted thinking patterns of stress-creating thinking, self-criticism/self-blame, and predictive thinking are predictors of depression. However, stress-creating thinking was the strongest predictor of depression. Stress-creating thinking, predictive thinking, and rigid thinking were predictors of anxiety in university students during online education and stress-creating thinking is the strongest predictor of anxiety as well. Moreover, the distorted thinking patterns of stress creating thinking, self-criticism/self-blame, and rigid thinking strongly predicted stress in university students engaged in distant education during the COVID-19 outbreak. Implications to Research and Practice: The study's findings emphasize the role of distorted thinking patterns in the stress experience of students during COVID and encourage teachers and universities to consider the findings while developing an online education system for the students. (C) 2022 Ani Publishing Ltd. All rights reserved.

Learn Programming In Virtual Reality? A Case Study of Computer Science Students

This paper presents the development of a new learning platform in Virtual Reality to create a more immersive and intuitive learning experience for introduction of programming courses at an intermediate level. This platform is designed to create a central hub for interactive courseware and facilitate distance learning in our post COVID world. Utilizing Virtual Reality, the application teaches specific topics in Computer Science using scripted animations, tutorials, and interactive games. A pilot study was conducted to evaluate the user experience and learning outcomes. Participants of this study reported they were more engaged and motivated in learning programing concepts. We found the virtual learning modules helped to explain concepts and provided better hands-on experiences. © 2022 IEEE.

RHEUMATOLOGY REFERRALS TO TERTIARY CARE HOSPITAL OVER A THREE MONTH PERIOD DURING COVID- 19 PANDEMIC: A QUALITY IMPROVEMENT PROJECT

Background/Aims During the COVID-19 pandemic, waiting times for routine new referrals to our tertiary rheumatology department was >20 weeks. Therefore, a quality improvement project (QIP) was undertaken to understand the nature of these referrals and develop an alternative option to rheumatology review. Our aim was to reduce waiting times and improve patient experience by better integrating primary, secondary and therapy services, as well as provide additional triage options. Methods We conducted a retrospective analysis of all routine referrals over a 3- month period (1st April to 30th June 2020). Urgent referrals including GCA, CTD and EIA were excluded. Results A total of 92/143 (64%) patients were referred, a more significant reduction than normal due to the pandemic. Median age [IQR] was 39.5 [28-66.25] years and most referrals (79%) were from primary care. Table 1 represents information included in the referrals. Thirty-one patients had previously undergone a rheumatology review, of which 11 (35%) were seen in our department. Of these, 18/31 (58%) patients had a diagnosis of Hypermobility Spectrum Disorder (HSD) or fibromyalgia. The commonest reason for re-referral was worsening of existing symptoms (n=11, 35%), with no suggestive of an alternative diagnosis. Conclusion Our QIP identified a variation in the quality of referrals and that a high proportion of referrals concerned HSD and fibromyalgia, with many rereferred due to exacerbation of their existing disease. Based on this, we conducted a regional GP trainee educational session and highlighted: i) key features in investigation and management of common rheumatological conditions ii) vital information to include in referrals based on presenting complaint(s) and working diagnosis. We developed a pathway for patients previously diagnosed with fibromyalgia or HSD in our department and re-referred with worsening symptoms, to be triaged into a newly set up weekly specialist MSK physiotherapy-led clinic with rheumatology supervision. Future work will involve re-assessing routine new-referral waiting times and evaluating pre- and post-physiotherapy intervention MSK and quality of life scores, with the aim of formulating a business case to conduct this clinic on a permanent basis. We hope incorporating this pathway will lead to improved patient outcomes and ease some departmental management decisions. (Table Presented).

PSYCHOMETRIC EVALUATION OF THE MODIFIED “FEAR OF COVID-19” SCALE

This study was designed to modify the recently developed “Fear of COVID-19” scale (FCV-19S) as a diagnostic criterion and to evaluate its psychometric properties and potential to predict risk of psychological problems. Through an e-questionnaire, data for this study were collected from 1,317 university students from 49 universities in Bangladesh. The modified “Fear of COVID-19” scale (MFCV-19S) showed good internal consistency (ω =.867) and concurrent validity;there was significant association with anxiety and depression. The unidimensionality was confirmed by an acceptable average variance extracted (0.49) and construct reliability (.87). The MFCV-19S differentiates fairly between persons with and without anxiety disorder, using an optimized cut score of ≥ 8 (93% sensitivity and 78% specificity). The multivariate analysis also suggested that MFCV-19S can significantly predict risk of mental health problems. The results indicated that the MFCV-19S is an efficient and valid psychometric tool for screening fear of COVID-19 among students and could be used for general people © Copyright: The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 <http://creativecommons.org/licenses/by/4.0/>)

Public perception, knowledge and readiness for behaviour Modification in covid-19 pandemic in Pakistan

Objective: To explore individuals‟ knowledge and perception of the COVID-19 virus pandemic, views on imposed governmental restrictions and engagement rates in mandatory behavioural restrictions in a cohort of Pakistan under or post-graduate students. Methodology: A web-based cross-sectional survey was conducted and 358 participants completed the online questionnaire. Results: Participants had a high awareness of several potential symptoms of COVID-19 with shortness of breath (99%), a dry cough (96%), and fever (94%). A significant minority of participants stated that despite governmental restrictions, they planned to continue to engage with family members (not resident with them if asymptomatic (21.5%). Conclusion: Clear dissemination of information from appropriate governmental sources is advised to support on-going engagement in effective preventative strategies for the COVID-19 pandemic in Pakistan. © 2022, Pakistan Medical Association. All rights reserved.

Evaluation of anticholinergic cognitive burden in patients with post-intensive care syndrome

INTRODUCTION: Post-Intensive care syndrome (PICS) is the culmination of cognitive, psychological and physical issues critical illness survivors encounter. More than half of critically ill patients may experience cognitive impairment after hospital discharge. There is limited information on cognitive impairment surrounding PICS in COVID-19 survivors. This study aims to evaluate the impact anticholinergic cognitive burden has on cognitive function in the COVID-19 survivor patient population. METHODS: This retrospective, observational cohort study included patients from the post-intensive care survivor clinic at Indiana University Health that were discharged from April 2020 to March 2021. Patients were excluded if they did not have an admitting diagnosis of respiratory failure secondary to COVID-19 or did not have discharge documentation. Cognitive impairment was evaluated using the Montreal Cognitive Assessment (MoCA). Demographics and anticholinergic cognitive burden (ACB) scores were compared between patients with cognitive impairment (MoCA < 26) versus those without cognitive impairment (MoCA > 26). RESULTS: Twenty-six patients were included in this evaluation. Twelve patients had cognitive impairment and fourteen patients did not have cognitive impairment. ACB score at discharge for those with and without cognitive impairment had a median (IQR) score of 2.5 (0.75-4.25) and 1 (0.25-2), respectively (p=0.208). The patients with cognitive impairment had a median (IQR) ACB score at the start and end of the initial appointment of 1.5 (0.75-2.5) and 1.5 (0.75-2.25), respectively. The median (IQR) ACB score at the start and end of the initial appointment for patients without cognitive impairment was 1 (0-1.75). Delirium was reported 7 patients (58%) with cognitive impairment compared to 5 patients (36%) without cognitive impairment (p=0.431). The Charlston comorbidity score was higher in those with cognitive impairment, with a median (IQR) score of 3.5 (2.75-5) compared to 0 (0-2) for those without cognitive impairment (p=0.002). CONCLUSIONS: The difference in ACB scores at discharge was not statistically different between patients with and without cognitive impairment. Patients with cognitive impairment post ICU discharge tended to have a higher Charlston comorbidity score.

Outcomes of older covid-19 patients in acute care at home, southern hsc trust, northern ireland, from march-june 2020

Introduction: The older population has been most affected by COVID-19, with mortality rates of around 27%. The Acute Care at Home (ACAH) team aims to improve outcomes in the older population by preventing hospital admission or facilitating early discharge, allowing patients to be treated in their own environment. During the COVID-19 pandemic, the ACAH team administered oxygen therapy, antibiotics, anticipatory medications and other vital interventions to combat the ill effects of COVID-19. Method: An observational approach has been used in this study. Patients were included if they were admitted to ACAH during March-June 2020 for treatment of COVID-19. Biochemistry, oxygen saturations and co-morbidities are among the studied parameters. Lymphocyte count and serum magnesium were compared with a non-COVID-19 cohort. Trends within parameters and associated mortality were analysed and tabulated. Results: 70% of admissions were lymphopenic, whilst 54% were hypoxic. There was a 28-day mortality rate of 35%, with an 18% increase in mortality rate when comparing residence in long-term care facilities (LTCF) to personal residence. All patients had existing co-morbidities. Conclusion: The data indicates that hypoxaemia, hyperferritinaemia and hypermagnesaemia are associated with early mortality in the older population infected with COVID-19. National Early Warning Score and frailty score are predictive of mortality in this cohort, with higher scores correlating to worse outcomes. Those living in LTCF are at an increased risk of mortality. However, ACAH mortality rates are comparable to those admitted to hospital, validating the concept of ACAH. The highlighted trends can be used to improve outcomes in future admissions.

Prognosticating the effect on Unemployment rate in the post-pandemic India via Time-Series Forecasting and Least Squares Approximation

The current paper aims to analytically visualize the future outcomes that the post-pandemic India might have in store for its citizens. We use time series forecasting on various collected data and combined the statistics of economics-deciding parameters to forecast the trends that might be prevalent in the next year. Since, the data contains a single anomalous trend, even the Prophet model could not learn this property from the data since this trend is not seasonal in nature. The current study proposes a novel architecture to deal with these rare unusual trends by combining two models - one learning normal usual patterns and the other getting trained on usual as well as rare anomalous patterns. It could help in dealing with sudden hike patterns like due to COVID-19 in the data, and lead to better forecasting on future timeframes. We combined the results of two distinct time-forecasting models trained on two sets of data of varying timeline lengths, using parameters obtained from Least Squares Approximation (LSA). The LSA helps us find an approximate vector approximation so as to obtain a model performing closely to the actual. © 2021

Determination of COVID-19 Patients Using Machine Learning Algorithms

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied. The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naive bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.

Positive rate of COVID 19 in the setting of GGH Ghulam Muhammadabad

Background: The corona virus disease also known as COVID-19 has opened gates to a lot of research about detection, treatment and prevention in the last past year due to lack of information regarding the SARS-CoV-2 virus. PCR corona via nasopharyngeal swab is the standard method of detection in our set-up. Materials and methods: Nasopharyngeal swabs were taken by ENT department using precautions and following proper SOPs. Swabs were sent for rRT-PCR tests. Data was collected and analyzed. All the subjects meeting the inclusion criteria were included in study. Data was analyzed using SPSS version 20. Frequency and percentage was calculated for gender. Positivity rate was calculated using CDC formula. Study design: - Descriptive studies Place and Duration of Study: - Government General Hospital, Ghulam Muhammadabad, Faisalabad. 889 samples were taken from 7.1.2020 to 6.12.2020. Results: Standard PCR for corona test by nasopharyngeal route was taken of suspected corona patients and of patients with contact with corona positive patients. 894 samples were taken from 7.1.2020 to 6.12.2020. 263 were positive for corona. The calculated positive rate is 29.41%. Conclusions: Calculation of positive rates is a very easy metric to give us a quick overview of the spread of the virus.