Performance Evaluation of State-of-The-Art 2D Face Recognition Algorithms on Real and Synthetic Masked Face Datasets

Face recognition systems based on Convolutional neural networks have recorded unprecedented performance for multiple benchmark face datasets. Due to the Covid-19 outbreak, people are now compelled to wear face masks to reduce the virus's transmissibility. Recent research shows that when given the masked face recognition scenario, which imposes up to 70% occlusion of the face area, the performance of the FR algorithms degrades by a significant margin. This paper presents an experimental evaluation of a subset of the MFD-Kaggle and Masked-LFW (MLFW) datasets to explore the effects of face mask occlusion against implementing seven state-of-the-art FR models. Experiments on MFD-Kaggle show that the accuracy of the best-performing model, VGGFace degraded by almost 40%, from 82.1% (unmasked) to 40.4% (masked). On a larger-scale dataset MLFW, the impact of mask-wearing on FR models was also up to 50%. We trained and evaluated a proposed Mask Face Recognition (MFR) model whose performance is much better than the SOTA algorithms. The SOTA algorithms studied are unusable in the presence of face masks, and MFR performance is slightly degraded without face masks. This show that more robust FR models are required for real masked face applications while having a large-scale masked face dataset. © 2023, Penerbit Akademia Baru. All rights reserved.

Block Toeplitz Matrices: Multiplicative Properties

Given A, B, C, and D, block Toeplitz matrices, we will prove the necessary and sufficient condition for AB - CD = 0, and AB - CD to be a block Toeplitz matrix. In addition, with respect to change of basis, the characterization of normal block Toeplitz matrices with entries from the algebra of diagonal matrices is also obtained.

Relationship of clinical symptoms, comorbidities and treatment options along with disease outcomes in individuals tested for COVID-19 in Pakistan: a national survey

Background: This web-based survey is done to collect and assess data from people tested for COVID-19 with PCR in Pakistan. Methods: This 3-month study is a cross-sectional online survey, conducted by Pakistan Islamic Medical Association (PIMA), Health Research Advisory Board (HealthRAB) and National Institute of Health (NIH). Data collection was done using Google Forms. People who were tested for COVID-19 using Polymerase Chain Reaction (PCR) were included in the study. The sample size of the study was 1,537. SPSS version 22 was used for data analysis. Results: Majority of the respondents belonged to the age group 20 - 39 years. The most common symptoms found were fever 633 (41%), cough 534 (34%), generalized body aches 432 (28%) and sore throat 392 (25%). The mean COVID-19 mental health score was 3.59 (SD: 5.808, range: 0-18). Treatment with antibiotics and painkillers had a strong correlation (p-value < 0.05) with the disease outcomes. The disease outcomes had moderate correlation (p-value < 0.05) with anti-allergy, steroids, plasma and oxygen therapy, and weak correlation (p-value < 0.05) with Antiviral and Antimalarial therapy. Out of the total respondents, 561 (36.1%) were cured from COVID-19, 14 (0.9%) were expired during/after hospitalization, 15 (1%) were still infected and 962 (62%) were not infected. Conclusion: Pakistani population has a better cure rate than some of its neighboring countries. However, further research in this area is required to draw a definite conclusion.

Evaluation of Factors Causing a Delay in Management and Hospital Stay in Diabetic Foot Amputation Patients: A Prospective Cohort Study

OBJECTIVE: To determine the rate of different amputation levels in diabetic foot patients and the incidence of repetitive foot surgeries and evaluate the factors causing a delay in hospital stay and amputation of patients. METHODOLOGY: This prospective cohort study was conducted in Dr. Ruth K.M. Pfau, Civil Hospital Karachi, Pakistan. The study selected 375 participants from the clinic's daily patient inflow from October 2021 to March 2022 using a non-probability consecutive sampling technique. Those who had a delay in hospital stay and amputation were further followed up from May-October 2022. The chi-square test and Kruskal Wallis test (p-value <0.05) were used to correlate the effect of the level of lower limb amputation and the cause of delay in amputation using SPSS version 24.0. RESULT(S): Total 246(65.60%) were males and 129(34.40%) were females. Toe amputation was the most commonly seen amputation in 173(46.1%) participants. About 168(44.8%) patients had some in-hospital delay stay during their treatment. Preoperative hurdles (Uncontrolled RBS, Osteomyelitis, etc.) were the most common factor causing an in-hospital delay in 92(24.5%) patients. The level of amputation performed was found to be statistically significant with factors causing a delay in hospital stay through chi-square (p=0.003*) and Kruskal Wallis test H (2) statistic= 13.3, df = 3, H (2), P=0.004*). CONCLUSION(S): Diabetic foot is a frequent cause of amputation globally, majorly in developing countries like Pakistan. On-time provision of treatment to these patients can decline the global amputation rate due to diabetic foot ulcers.Copyright © 2023 Syeda Anjala Tahir.

INSIGHT OF PANDEMIC COVID-19: DEVELOPMENTS AND CHALLENGES

This review focuses on the characteristics of coronavirus disease-19 (COVID-19) including virus structure, ecoepidemiology and pathophysiology, signs and symptoms in infected people, and data on virus pathogenicity, severity, and survivability in COVID-19 infected patients. The emphasis is on immunological reactions, diagnosis, prophylactic methods, and the zoonotic significance of COVID-19. The authors feel that the review's contents will be valuable to epidemiologists, virologists, public health officials, diagnosticians, laboratory workers, environmentalists, and socioeconomic experts. It has information on the many types of coronavirus variants, the disease situation in Pakistan and the WHO criteria for COVID-19 prevention is given. Moreover, lessons learned from the COVID-19 pandemic are also outlined.

Deep Rank-Based Average Pooling Network for Covid-19 Recognition

(Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-cony rank-based average pooling module (NRAPM) was proposed in which rank-based pooling-particularly, rank-based average pooling (RAP)-was employed to avoid overfitting. Third, a novel DRAPNet was proposed based on NRAPM and inspired by the VGG network. Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis. (Results) Our DRAPNet achieved a micro-averaged F1 score of 95.49% by 10 runs over the test set. The sensitivities of the four classes were 95.44%, 96.07%, 94.41%, and 96.07%, respectively. The precisions of four classes were 96.45%, 95.22%, 95.05%, and 95.28%, respectively. The F1 scores of the four classes were 95.94%, 95.64%, 94.73%, and 95.67%, respectively. Besides, the confusion matrix was given. (Conclusions) The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases. The RAP gives better results than four other methods: strided convolution, l(2)-norm pooling, average pooling, and max pooling.

Impact of severe acute respiratory syndrome-coronavirus-2 pandemic on postgraduate medical education in armed forces medical services institutions: A students' perspective

Introduction: The Severe acute respiratory syndrome coronavirus 2 pandemic situation brings us the opportunity to test the strength and limitations of our health delivery system. Residents being the backbone of quality-health-delivery of any institute have taken the brunt. Materials and Methods: A cross-sectional self-administered questionnaire-based survey was used to assess the effect on medical training and stress of postgraduate residents in clinical specialties of armed forces institutions.Results: 266 valid responses were analyzed. Eighty-seven percent of residents felt their surgical/procedure-related training was affected. Bedside/clinical training was found to be affected by 92% and theoretical learning by 78%. A significant difference was found between residents in medical and allied specialties and residents in surgery and allied specialties (81% vs. 96.3%) with regard to the negative effect of the COVID-19 pandemic on surgical/procedural skills training (P < 0.05). There was a significant difference in the likelihood of being posted for COVID duties based on gender (P = 0.01) and year of the course (P = 0.004). Posting on COVID duties did not significantly affect surgical, clinical, or theoretical training. Of the respondents, 37%, 49%, and 14% had a mild, moderate, and severe increase in stress, respectively. 18%, 52%, and 30% experienced mild, moderate, and severe increased stress among family members. Gender, age, category, year of residency, or subject of specialization did not have any significant effect on the level of personal or family stress. Conclusion: This survey attempts to bring forth the effect of the pandemic on medical training schedules and stress among residents. Such surveys would enhance understanding and bring solutions to the problem that the pandemic has brought.

Efficient numerical simulations based on an explicit group approach for the time fractional advection–diffusion reaction equation

The time-fractional advection–diffusion reaction equation (TFADRE) is a fundamental mathematical model because of its key role in describing various processes such as oil reservoir simulations, COVID-19 transmission, mass and energy transport, and global weather production. One of the prominent issues with time fractional differential equations is the design of efficient and stable computational schemes for fast and accurate numerical simulations. We construct in this paper, a simple and yet efficient modified fractional explicit group method (MFEGM) for solving the two-dimensional TFADRE with suitable initial and boundary conditions. The proposed method is established using a difference scheme based on L1 discretization in temporal direction and central difference approximations with double spacing in spatial direction. For comparison purposes, the Crank–Nicolson finite difference method (CNFDM) is proposed. The stability and convergence of the presented methods are theoretically proved and numerically affirmed. We illustrate the computational efficiency of the MFEGM by comparing it to the CNFDM for four numerical examples including fractional diffusion and fractional advection–diffusion models. The numerical results show that the MFEGM is capable of reducing iteration count and CPU timing effectively compared to the CNFDM, making it well-suited to time fractional diffusion equations. © 2023, The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional.

Multi-branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.

Knowledge, Attitude and Practice Towards Corona Virus Disease 2019 (Covid-19) among Medical Students: A Cross-Sectional Study

Introduction: Corona virus disease 2019 (COVID-19), was recognized and has caused serious illness and numerous deaths. The ultimate scope and effect of this outbreak are unclear at present as the situation is rapidly evolving. The disease causes respiratory illness (like the flu) with main clinical symptoms such as a dry cough, fever, and in more severe cases, difficulty in breathing. Objective(s): To assess knowledge, attitude, and practice of medical students towards corona virus disease 2019 (COVID-19). Material(s) and Method(s): An online cross-sectional survey was conducted among undergraduate medical students in India from September 2020 to February 2021. Participants were recruited using a snowball sampling technique and all data were collected via an online self-reported questionnaire using Google Forms (http://forms.google.com/) as the data collection period coincided with implementation of the COVID-19 lockdown policy in India. Socio-demographics characteristics, social interaction history, information-seeking behavior, as well as knowledge, attitude, and practice toward COVID-19 were collected through a self-reported questionnaire. A p-value of <0.04 indicated statistical significance. Result(s): A total of 2000 eligible participants completed the survey, 71.5% of whom were female, and their mean age was 19.4 years old (SD = 2.1). Almost all had sufficient knowledge (87.1%) and good preventive practice (93.6%) towards COVID-19;however, there was also a rather low level of positive attitude recorded, at 65.7%. The multivariable logistic regression analysis showed that the female participants, and the receiving of information from the official websites, reported a significantly higher level of good practice. Besides, students who had a high level of sufficient knowledge and positive attitude towards COVID-19 were more likely to have good preventive practices (All p<0.001). Conclusion: Many undergraduate medical students in India had positive attitude and practice against COVID-19, yet only a few had adequate knowledge. This warrants further interventions to keep them updated with COVID-19 evidence to maximize their potentials in raising public awareness on COVID-19.Copyright © 2020 Ubiquity Press. All rights reserved.

Covid-19 pandemic and public health challenges in Jammu & Kashmir

COVID-19 pandemic has emerged as prime health challenge of 21st century forcing policy makers, health experts and governance institutions world over to revisit and re-invigorate public health policies through inter-institutional collaborations. Subsequent global lockdowns caused unprecedented shock to world economies, downslide of socio-economic development, concern for public safely, emphasis on augmentation of health infrastructure, capacity building of health care providers and development of effective Corona Virus containment strategies. Health institutions world over are grappling to control spread of the infection through Symptomatic Target Testing, Cluster Testing and Phased Vaccination. Multiple vaccines have been developed with varied efficacy, cost concerns and involvement of logistic issues;leading to vaccine-multilateralism and re-emphasis on universalization of public health policies under Sustainable Development Goals (SDGs) mechanism. This paper aims to assess impact of this grievous pandemic on public health sector of Jammu & Kashmir, explore challenges faced by public health institutions, analyze effectiveness of government interventions and suggest measures for revival of public health care services in the region.Copyright © 2021 JK Practitioner. All rights reserved.

COVID-19 pandemic and the dynamics of major investable assets: What gives shelter to investors?

The rapid spread of coronavirus (COVID-19) significantly destabilized the global financial and economic conditions, prompting investors to seek safer investments for their portfolios. This study examines the contagion effect and dynamic relationship between DJI, DJIM, gold, silver, oil, bitcoin, ethereum and cryptocurrency index using data obtained before (18 June 2019–29 January 2020), during the first phase (30 January – 11 September 2020) and after the first phase of COVID-19 pandemic (12th September 2020 - 3rd January 2022). We employed three main methodologies: MGARCH-DCC, MODWT and Wavelet Coherence analysis, to investigate the safe haven property of major investable assets during the pandemic. The findings revealed that gold offers a safe trading opportunity due to its lower volatility and correlation across the different stock market investment horizons (with a few exceptions), contrary to other assets, which exhibits high volatility and correlation. The phase pattern demonstrated the existence of a dynamic asymmetric relationship between the studied investable assets and stock market returns. Furthermore, the study also found that the dynamic lead-lag relationship of investable assets with the stock market returns varies depending on the investment horizons before and during the pandemic. © 2023 Elsevier Inc.

Post-Traumatic Stress Disorder (PTSD) Among Healthcare Workers of Tertiary Care Hospitals of Rawalpindi;Amid the COVID-19 Pandemic

Objective: To determine the prevalence of PTSD symptoms and its severity among HCWs, amid the COVID pandemic in a tertiary care setting. Study Design: Cross sectional analytic study. Place and Duration of Study: Pakistan Emirates Military Hospital, Rawalpindi Pakistan, from May to Aug 22. Methodology: The study was conducted on 173 healthcare workers of a tertiary care hospital. The sample size was calculated using the Rao-soft calculator. Validated questionnaires such as the Impact of Event Scale-Revised (IES-R) and PTSD Checklist Civilian Version (PCL-C) were used to collect the data. Data was entered and analyzed by using Statistical Package for Social Sciences (SPSS) version 26. Results: Out of 173 participants, majority of participants 90(52%) were male and single 103(63%). Mean age of the participating HCWs was 27(SD= 2.3). HCWs performing duties the in COVID-19 ward for one year were 66(38.2%) and majority were from Emergency medicine 61(35.3). Almost 150(86.71%) of HCWs did not experiencing any PTSD symptoms and those exhibiting a higher severity of symptoms were only 5(2.9%). There was no statistically significant difference in total and sub scales mean scores of IES-R among males and females (p=0.28). Conclusions: Our study concluded that prevalence of symptoms of PTSD was significantly low in Health Care Workers despite of the fact, majority of doctor participants were working in COVID-19 ward for one year. The symptoms were evident among healthcare professionals, however there was modest severity. © 2022, Army Medical College. All rights reserved.

Impact of COVID-19 on fish consumption and nutrition intake—An empirical study

This study investigates the effects of COVID-19 on fish consumption and nutrition intake based on a random survey of 247 fish consumers in Bangladesh. The Propensity Score Matching technique is used to compare fish consumption and fish-sourced nutrition intake between two groups of consumers before and during COVID-19. The result shows that 38% overall reduction in fish consumption for the low-income group compared to lower-middle, upper-middle- and high-income groups. Furthermore, per capita consumption of culture and capture fish species decreased significantly for low-income, lower-middle-income, and upper-middle-income groups of consumers. It indicates that nutrition and mineral intake have reduced sharply as well Higher energy and K reduction are observed for Pangasius hypophthalmus among different culture fish species while energy and Ca reduction were higher for Wallago attu and Mystus vittatus respectively. Therefore, the government might place a greater emphasis on excluding the food supply chain from lockdown restrictions during a COVID-19-like pandemic. © 2023 The Authors. Journal of the World Aquaculture Society published by Wiley Periodicals LLC on behalf of World Aquaculture Society.

Knowledge, attitudes and practices towards COVID-19 among populations of Islamabad and Rawalpindi during lockdown: A cross-sectional survey

In Pakistan, the first confirmed case of COVID-19 was reported on 26 February 2020, having the travel history from Iran. Islamabad and Rawalpindi have also been affected by COVID-19 epidemic. On 23 March 2020, the Government of Pakistan has declared smart lockdown all over the country including Islamabad and Rawalpindi. The aim of the study was to identify the status of the knowledge, attitudes and practices regarding COVID-19 among the general population of the twin cities (Islamabad and Rawalpindi) in Pakistan during the COVID-19 outbreak. A cross-sectional web-based survey was conducted from 5 to 19 May 2020, the week during smart lockdown in Islamabad and Rawalpindi. Demographic characteristics were compared with independent-samples t-test, one-way, or Chi-square test. Multivariable linear regression analysis was used to identify factors associated with low knowledge score. Data analyses were conducted with SPSS version 21.0. A total of 1,282 participants completed the questionnaire. Among this final sample, the average age was 30.65 years. Among the survey respondents, 680 (53%) were women, 1096 (86%) held a bachelor's degree or above, 634 (50%) were engaged with the government and private sector and 606 (47%) were married. The overall correct rate of knowledge was 70%. The majority of the respondents agreed that COVID-19 will finally be successfully controlled (59%). Most of the participants had not visited any crowded place (74%) and 95% responded that they have reduced their outdoor activities. In response to precaution measures, 86% stated that they would isolate themselves if they ever felt a fever or cough. The study findings suggest that residents of the two cities have reasonable levels of knowledge on COVID-19. However, it is necessary to launch health education and awareness campaigns to improve the knowledge and practices about COVID-19, to control its transmission. © 2022 The Author(s).

Drones-aided Asset Maintenance in Hospitals

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.

Major Drugs Used in COVID-19 Treatment: Molecular Mechanisms, Vali-dation and Current Progress in Trials

Background: Currently, the present world is facing a new deadly challenge from a pandemic disease called COVID-19, which is caused by a coronavirus named SARS-CoV-2. To date, no drug or vaccine can treat COVID-19 completely, but some drugs have been used primarily, and they are in different stages of clinical trials. This review article discussed and compared those drugs which are running ahead in COVID-19 treatments. Method(s): We have explored PUBMED, SCOPUS, WEB OF SCIENCE, as well as press releases of WHO, NIH and FDA for articles related to COVID-19 and reviewed them. Result(s): Drugs like favipiravir, remdesivir, lopinavir/ritonavir, hydroxychloroquine, azithromycin, ivermectin, corticosteroids and interferons have been found effective to some extent, and partially approved by FDA and WHO to treat COVID-19 at different levels. However, some of these drugs have been disapproved later, although clinical trials are going on. In parallel, plasma therapy has been found fruitful to some extent too, and a number of vaccine trials are going on. Conclusion(s): This review article discussed the epidemiologic and mechanistic characteristics of SARS-CoV-2, and how drugs could act on this virus with the comparative discussion on progress and drawbacks of major drugs used till date, which might be beneficial for choosing therapies against COVID-19 in different countries.Copyright © 2022 Bentham Science Publishers.

Pharmacovigilance practices in South Asian Association for Regional Cooperation countries: the need for collaboration

Objectives: One-fifth of the world's population lives in eight countries that constitute the South Asian Association for Regional Cooperation (SAARC). There is very little coordination among SAARC countries regarding the harmonization of pharmaceutical regulations and medicines safety. Pakistan, India and Bangladesh have experienced medicine-related tragedies where many patients have died. This study aims to examine current pharmacovigilance activity in the SAARC region to improve pharmacovigilance practices and to make recommendations for building a platform for collaboration to improve the safety monitoring of medicines in the region. The current review utilized secondary data. We reviewed the official websites of all SAARC countries' national regulatory authorities for pharmacovigilance-related information. A data set with eleven pharmacovigilance indicators were gathered and synthesized. Key Findings: All eight SAARC member countries have pharmacovigilance systems with full membership in the WHO Program for International Drug Monitoring. Out of eleven pharmacovigilance indicators, India met ten;Pakistan, Bangladesh and Bhutan nine;Maldives and Afghanistan seven;Nepal and Sri Lanka five. The SAARC countries do not have a harmonized pharmacovigilance system or centralized database. Due to positioning in different WHO regions, it is proposed to create a consortium on medicine safety among SAARC countries like other regional organizations of the world to strengthen the pharmacovigilance systems and harmonize the pharmacovigilance practices among member countries. Summary: To improve the quality of medicines and to strengthen regional medicine safety, the SAARC secretariat should consider forming a technical group of all member countries' regulatory authorities.Copyright © 2022 The Author(s). Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved.

Idea Generation using Transformer Decoder Models

Our work aims to generate new ideas to explore in a specific domain using generative language models. For example, doctors can write about known symptoms as cues to the system, and then the system will generate ideas based on the cues. Similar scenarios can be thought of for other scientific domains. We used transformer-based decoders, especially GPT3-based transformer decoders, as the language models and generators. As the data, we used COVID-19 open research dataset [18]. We finetuned GPT-NEO-125M and GPT-NEO-1.3B models with 125 million and 1.3 billion parameters, respectively. The later model generated more coherent text and could link ideas relevant to the same problem better. We report here our findings with examples generated from our finetuned models. © 2022 ACM.

Analysis of COVID-19's Effects on the Incidence of Chronic Respiratory Symptoms in KPK

Introduction: Pakistan has a high prevalence of chronic respiratory disorders, including bronchial asthma and chronic obstructive pulmonary disease (COPD). Objective(s): Finding the effect of COVID-19 on chronic respiratory disease in Pakistan is the study's key goal. Material(s) and Method(s): From February 2021 to December 2021, this cross-sectional research was carried department of pulmonology at HMC hospital Peshawar A specified To differentiate between the COVID-19 era and the period preceding it, a set of criteria in the form of a questionnaire was applied. Result(s): A total of 157 participants contributed to the data set. Patients who tested positive for COVID 19 were additionally asked about their experiences with respiratory co morbidities. More than a third of respondents mentioned COPD as a co morbidity;many also mentioned bronchial asthma, ILD, and tuberculosis (TB). Conclusion(s): COVID-19 would certainly increase chronic respiratory disorders in Khyber Pakhtunkhwa. The pandemic might increase respiratory disorders, strain health systems, and cost people impacted. Increase public health awareness and ensure chronic respiratory illness patients get proper treatment and resources to address these issues. To decrease the pandemic's effect on Khyber Pakhtunkhwa's population, early respiratory disease identification and treatment techniques are needed.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.