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PurposeThe purpose of this study is to investigate the impact of Corona Virus Disease 2019 (COVID-19) on the outcome of construction projects and explore the moderating effects of emerging technologies on the relationship between COVID-19 and construction project outcomes. Design/methodology/approachData for the study was collected through a Web-based, semistructured questionnaire. The responses of 62 construction practitioners were analyzed using a hierarchical linear regression model. The model consists of 16 independent variables, three control variables (organization size, organization type and project size), one moderator (adoption level of emerging technologies) and three dependent variables (project time, project cost and project quality). FindingsThe study confirms the negative significant impact of the COVID-19 pandemic on the performance of construction projects. It also identifies the significant moderating effects of emerging technologies in mitigating the impact of COVID-19 on construction projects. Further, it shows a significant increase in the application of emerging technologies in construction projects during the COVID-19 pandemic. Based on the findings related to the moderating impact of the technology, this study provides a clear set of recommendations for construction firms, public sector and research community in combating the unavoidable situation similar to the COVID-19 pandemic in the future. Originality/valueTo the best of the authors' knowledge, this is the first study to identify the moderating role of technology on the impact of COVID-19 on the performance of the construction sector in Pakistan. The findings can also be used for the construction sectors of other developing countries.
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Background/Aims The impact of the pandemic on the incidence and management of inflammatory arthritis (IA) is not understood. Routinely-captured data in secure platforms, such as OpenSAFELY, offer unique opportunities to understand how IA was impacted upon by the pandemic. Our objective was to use OpenSAFELY to assess the effects of the pandemic on diagnostic incidence and care delivery for IA in England, and replicate key metrics from the National Early Inflammatory Arthritis Audit. Methods With the approval of NHS England, we used primary care and hospital data for 17 million adults registered with general practices using TPP health record software, to explore the following outcomes between 1 April 2019 and 31 March 2022: 1) incidence of IA diagnoses (rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, undifferentiated IA) recorded in primary care;2) time to first rheumatology assessment;3) time to first prescription of a conventional synthetic DMARD (csDMARD) in primary care, and choice of first csDMARD. Results From 17,683,500 adults (representing 40% of the English population), there were 31,280 incident IA diagnoses recorded between April 2019 and March 2022. New IA diagnoses decreased by 39.7% in the early months of the pandemic. Overall, a 20.3% decrease in IA diagnoses was seen in the year commencing April 2020, relative to the preceding year (5.1 vs. 6.4 diagnoses per 10,000 adults, respectively). Further decreases coincided with rising COVID-19 numbers, before returning to pre-pandemic levels by the end of the study period. No rebound increase in IA incidence was observed as of April 2022. The median time from referral to first rheumatology assessment was shorter during the pandemic (18 days;IQR 8-35 days) than before (21 days;9-41 days). The proportion of patients prescribed csDMARDs in primary care was comparable to before the pandemic;however, fewer people were prescribed methotrexate or leflunomide, and more were prescribed sulfasalazine or hydroxychloroquine. Conclusion IA diagnoses decreased markedly during the early phase of the pandemic;however, the impact on rheumatology assessment times and DMARD prescribing was less marked than might have been anticipated. This study demonstrates the feasibility of using routinelycaptured, near real-time data in the secure OpenSAFELY platform to benchmark care quality on a national scale, without the need for manual data collection.
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Purpose: At the outbreak of the COVID-19 pandemic, the absence of pharmaceutical agents meant that policy institutions had to intervene by providing nonpharmaceutical interventions (NPIs). To satisfy this need, the World Health Organization (WHO) issued policy guidelines, such as NPIs, and the government of Pakistan released its own policy document that included social distancing (SD) as a containment measure. This study explores the policy actors and their role in implementing SD as an NPI in the context of the COVID-19 pandemic. Design/methodology/approach: The study adopted the constructs of Normalization Process Theory (NPT) to explore the implementation of SD as a complex and novel healthcare intervention under a qualitative study design. Data were collected through document analysis and interviews, and analysed under framework analysis protocols. Findings: The intervention actors (IAs), including healthcare providers, district management agents, and staff from other departments, were active in implementation in the local context. It was observed that healthcare providers integrated SD into their professional lives through a higher level of collective action and reflexive monitoring. However, the results suggest that more coherence and cognitive participation are required for integration. Originality/value: This novel research offers original and exclusive scenario narratives that satisfy the recent calls of the neo-implementation paradigm, and provides suggestions for managing the implementation impediments during the pandemic. The paper fills the implementation literature gap by exploring the normalisation process and designing a contextual framework for developing countries to implement guidelines for pandemics and healthcare crises. © 2023, Muhammad Fayyaz Nazir, Ellen Wayenberg and Shahzadah Fahed Qureshi.
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Aviation is one of the most severely impacted industries by COVID-19. The passenger boarding-process is not only a bottleneck but is also one of the riskiest processes for COVID-19 transmission. There is a need for a decision-support tool that can proactively test the impact of COVID-19 policies on the passenger boarding-process. We achieve this by developing an adaptable modeling approach to Discrete Event Simulation (DES) that simulates the process of boarding under different COVID-19 policies and boarding-strategies. DES model was created using time and motion studies, flightlogs and manuals. Programing-logic was created using n=29 subject-matter experts. As a demonstrator-case, we tested seven of the most common boarding-strategies under different COVID-19 stages: pre-COVID, COVID-19 stage 1 and 2. Preliminary-results show the COVID-19 transmission risk may be decreased with a trade-off: passenger-satisfaction may decrease due to an increase in boarding-time and waiting-time. Steffen's method was most-effective in minimizing COVID-19 risk but is the most difficult to implement. Reverse pyramid and Window Middle Aisle, while slightly less effective than Steffen's method, but overall, more-effective and easier to implement with minimal COVID-19 risk. For COVID-19 stage 1 and 2, boarding time increased up to 33% and 64%, respectively, in-comparison to baseline pre-pandemic conditions. Further, up to 1.5 and 6.6 seat and aisle interferences along with a jetway-seat time of up to 13 minutes were observed. The developed modeling approach serves as a direct response to ICAO's (International Civil Aviation Organization) need for a tool to proactively test and develop policies that minimize COVID-19 risk. © 2022 Operations and Supply Chain Management Forum. All rights reserved.
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Objective: To find the trend of seropositivity of antiSARS-CoV-2 antibodies in registered patients for COVID-19 in Dow Diagnostic Reference and Research laboratory Methodology: A total of 5247 patients were enrolled for SARS CoV2 antibodies analysis from 01 August to 31 December, 2020. Patient consent was attained and questionnaire forms were filled. Samples were tested for anti-SARS-CoV-2 antibodies on (Roche Cobase 601). Result(s): In 5247 patient's samples, seropositivity of SARS CoV2 was found in 2425 (46.2%) samples. Seroprevalence in males was 28.4% (n;1491) as compared to females, who showed 17% (n;934). The age group G3 (> 46 to 60 years) showed higher seropositivity (n = 604/1118, 54%) as related to other age groups. Out of total reactive patients, only 30% (n;727) reported recent symptoms while 70% (n;1697) were asymptomatic. Fever was observed to be the most common symptom followed by dry cough. The most commonly affected areas were East 2353 (44%), South 855 (16.01%), Malir 793 (14.85%), Center zone 589 (11.03%) and Korangi area 378 (7.08%). The frequency of seropositivity showed an increasing pattern in the six months from August 2020 to December 2020. It was found to be 11.5% in August, 13.6% in September, 13.9% in October, and 42.5% in December 2020. Conclusion(s): The trend of seropositivity revealed a gradual upward course in the duration of study period. Nearly, two thirds of the patients were asymptomatic indicating the fact that many individuals were silently exposed to the infection and developed antibodies through their natural defense mechanism.Copyright © 2023, Pakistan Medical Association. All rights reserved.
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The purpose of this research is to ascertain the effectiveness of using the e-learning method for a module in pediatric clerkship at the [redacted name] University Hospital, Karachi. The fourth-year undergraduate medical students, who rotates for eight weeks in Pediatric clerkship, participated in this study. It was a sequential (Quantitative-Qualitative) mixed-method study, which was conducted from May-August 2020. Students were divided according to their status of in-person rotation (Novice, Semi-expert, Expert). The quantitative component of the study consisted of pre and post-tests and pre-validated post-session feedback., while focused-group discussions were done to explore students' experiences. SPSS version 20.0 was used for quantitative data while qualitative data underwent content analysis. Fifty-nine participants (68.8%) were female. The intervention batch comprised of 102 students (41 Novice (40.2%), 21 Semi-expert (19.6%), and 40 Expert (39.2%)). Using paired t-test analysis between pre and post-test scores of each session, it was discerned that there was indeed a positive effect on knowledge acquisition during each session, depicted by the improvement in test scores. The Semi-expert and Expert groups were merged for analysis. The Novice group was found to be statistically significant for only the common newborn problem session. The qualitative component explored students' views, and three main themes emerged, i.e., the effectiveness of online learning, barriers and challenges to online learning, and future goals to enhance online learning. In conclusion, E-learning is an effective way of continuing the process of delivering medical education, especially in unprecedented times. Technological enhancements will help carry the impact forward as a blended-learning pedagogical approach in undergraduate medical education. © The Author(s) 2023.
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Objective: This study aimed to define the challenges faced by medical students rotating in the orthopedics department and their suggestions regarding improvement during covid-19 pandemic. Study Design: A mixed method cross sectional study design. Place and Duration of Study: It was conducted on 4th and 5th year MBBS students at Shifa college of Medicine with clerkship rotation in the department of orthopedics from 16th March 2020 to 23rd August 2021. Materials and Methods: Students were enquired about their comfort levels while using the internet and computer for online sessions. Data was collected through an online questionnaire and analyzed using Google forms. Frequencies, percentages, and standard deviations were calculated for qualitative variables. Results: Out of 147 study participants, 64(43.4%) students strongly agreed that they had no difficulty and were extremely comfortable using internet and computer during covid-19 pandemic. Eighty-five (58%) students used online available reading material shared on Google classrooms and what's app groups. While only 23(16%) agreed to concentrate during online sessions. One hundred and eighteen (80%) agreed with a lesser desire to study for online classes as compared to on campus. Major problems faced by the students during the pandemic included very limited patient centered learning, limited hands-on experience, less interactive sessions, problems with internet connections, technology handling and class timing issues due to time zone differences. Conclusion: We conclude that our students faced lot of challenges during Covid-19 pandemic including internet issues, lack of awareness of technology, distractions because of family, siblings and homely environment and lack of conducive learning environment like learning at bedside. Flexible class timings, multiple breaks, recorded lectures and online interaction of real patients can improve online clinical learning. © 2022 by the Author(s).
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Lateral flow assays (LFAs;aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care medical testing and, most recently, to detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection are far inferior to their laboratory-based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of rapid tests. The developed platform is based on Arduino and utilizes low-cost far infrared, single-element detectors to offer sensitive and semi-quantitative results from commercially available rapid tests. The sensing paradigm integrated to the low-cost device is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and, subsequently, interpret them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude from 25 ng/mL to 5 ng/mL, but also offers the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples. © 2022 by the authors.
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COVID-19, caused by SARS - COV 2 virus, has afflicted approximately 62.3 million people worldwide, with 1.46 million deaths around the globe by the end of November 2020. In most cases, the cause of death has been due to acute pneumonia. However, there have been cases where patients developed pulmonary arterial hypertension leading to sudden death. The virus can affect the heart in previously healthy individuals also. The severe inflammatory response in the body can affect arteries exaggerating cardiac damage. Studies recommend monitoring the cardiac conditions of POST COVID-19 patients. This paper employs a convolutional neural network (CNN) model to learn features from the standard axial slice of high-resolution chest CT. The CNN captures variation in the pulmonary artery region to determine whether a patient is at high risk of developing pulmonary arterial hypertension or not. With 86.1 % classification accuracy, the model shows a promising future for studies related to POST COVID risk analysis of heart complications. © 2022 IEEE.
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Introduction: COVID-19 infection is expected to be associated with an increased likelihood of sexual dysfunction in males and females in both rural and urban population. Considering high transmission rate of COVID-19, sexual dysfunction can be a concern for the population. Objective(s): To assess the sexual dysfunction in COVID-19 recovered patients. Method(s): This cross-sectional study was carried out among 120 patients to assess the sexual dysfunction in COVID-19 recovered patients in IIMSR Medical College and Noor Hospital, Badnapur Dist. Jalna, Maharashtra during the period of January to June 2021.Patients recovered from COVID-19 was contacted after 2 months of recovery. Participants were contacted on telephone for questioning and Sexual Dysfunction Questionnaire (SDQ) was filled as per information provided by the participants. The collected data was entered in Microsoft excel sheet and analyzed by using appropriate statistical tests whenever necessary. Result(s): Out of 120 COVID-19 recovered patients who participated in the study, sexual dysfunction was seen in 54 (45%) participants. Total of 72 males participated in the study, of which 36 of them had score more than 45(50%), while from 48 female participants 18 had score more than 45(37.5%). Conclusion(s): Exposure to the COVID-19 pandemic and its consequences was associated with increased sexual dysfunctions. Both male and female population with different age groups were affected but in variable degrees. Copyright © 2022 Ubiquity Press. All rights reserved.
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Background: COVID-19 was announced as a pandemic issue globally on 11th March, 2020. In response to this situation, all educational activities including medical and clinical education in various colleges across the country were suspended on the 15th of March. So, online education emerged as an alternative method of teaching & learning to maintain continuity of education Aim: To evaluate the use of online learning modalities and to find their feasibility and usability in medical education. Method(s): A cross-sectional study was performed across the government and private medical colleges of Lahore. Eligible participants were undergraduate medical students from 10 medical colleges of Lahore. A questionnaire linked to a Google form was distributed to the medical students across 10 government and private medical colleges through different social platforms. Result(s): A total of 439 valid questionnaires were collected. 31.7% of students disagreed that interaction between students and teachers was possible through online teaching. Only 7.7% of students agreed that online learning can be used for clinical teaching of medical sciences, as compared to 35.8% who disagreed with this answer and 12.8% who were neutral. 23% of the students agreed that online learning was more convenient and flexible than traditional learning, while 24% disagreed and 21.4% were neutral in this regard. Only 19.8% of students had problems with poor internet services. Conclusion(s): As Pakistan has faced four waves of the COVID-19 which is not over yet due to the emergence of new strains. Due to vaccination of medical students medical education is back to conventional physical learning but online learning has gained importance as an effective alternate to continue learning processes in exceptional situations like COVID-19 pandemic. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.
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Early diagnosis of Covid-19 is a challenging task requiring congruous clinical medical imaging, which is a time- consuming process and suffers from accuracy problems due to variations between different laboratory results. The clinical symptoms of Covid-19 show resemblance with acute respiratory distress syndrome. The major clinical symptoms linked with this disease are fever, cough, headache, migraine, and breathlessness. Despite tremendous research going on, knowing the way of transmission and its early detection remains a mystery. There is no treatment as of now for this virus, so a lot of unprecedented containment and mitigation policies such as closure of business places, schools, and colleges, marriage gathering restrictions, transport restrictions, and social distancing are being employed. These policies are able to limit the transmission of covid-19, but are not always feasible. Steps must be taken to slow down the spread of this virus and make an early diagnosis of infection to save lives. This paper gives a clear idea about the introduction of Covid-19, its symptoms, post covid-19 symptoms, challenges posed by the virus, and proposed solutions for its early detection to slow down its rate of transmission. Methods: The proposed solution includes a clustering algorithm for massive contact tracing that helps to slow down the transmission rate, and automatic virus detection and classification network known as Convolutional Neural Networks (CNN) based upon deep domain transfer learning. Results: The pre-trained model VGG-19 is used and the hyperparameters of the model are tuned as per classification requirement by exploiting the concept of deep domain transfer learning. The model is implemented on publicly available chest radiography images and the system classifies the dataset as covid and non-covid images. The CNN achieves 97.35 % accuracy outperforming all the existing methods. A new concept of employing nanocoating and nano sprays is also introduced in the paper. Conclusion: To discuss various critical challenges posed by Covid-19. Addressing those issues and proposing various solutions. Proposing clustering machine learning algorithm for massive contact tracing. Developing automatic covid-19 detection and classification system based upon automatic feature detection. Providing solutions based upon nanotechnology to slow down transmission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Currently, the most common disease is the new coronavirus disease identified as COVID-19. Various techniques to identifying the COVID-19 disease have been offered. Computer vision techniques are widely used to classify COVID-19 with the use of chest X-ray images. Rapid clinical results may prevent COVID-19 from spreading and help doctors treat patients under high workload conditions. As the normal diagnosis phase of illness with a laboratory test is time-consuming and requires a well-equipped laboratory, the X-ray imaging technique is a fast and cheap diagnostic tool for COVID-19. Machine learning methods can enhance the diagnosis of COVID-19 as a decision support platform for radiologists. This chapter utilizes various convolutional neural network (CNN) models, including pretrained models, to classify X-ray images into three classes: COVID-19, pneumonia, and normal. CNN, a form of deep neural networks that have become dominant in various computer vision tasks, attracts interest across various domains, including radiology. Pretrained models on ImageNet are good at detecting high-level features such as edges and patterns. These models understand certain representations of features, which can be reused. Also, deep classifiers have shown promising results in many kinds of work across various domains. We drew some useful results from these classifiers, which could be used faster when detecting COVID-19. Experimental results showed that the accuracy of the VGG19 classifier is 97.56%. © 2021 Elsevier Inc. All rights reserved.
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Viral infectious diseases such as Covid-19 present a major threat to public health. Despite extreme research efforts, how, when and where such new outbreaks appear is still a source of substantial uncertainty. Deep learning (DL) is playing an increasingly important role in our lives. This paper presents one of the popular deep learning technique, Long Short Term Memory (LSTM) for prediction of Corona-Virus cases. The handcrafted feature extraction of traditional methods is less scalable on large data-sets, but deep learning algorithms perform extremely well on large data-sets, because of automatic feature extraction. Deep learning has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. This paper highlights the approaches where deep learning can be helpful to tackle the Covid-19 virus and similar outbreaks. This paper also discusses the structure and functioning of Covid-19. The utilization of different deep learning concepts like Convolutional Neural Networks, Transfer Learning for this pandemic is also highlighted. © 2022 University of Bahrain. All rights reserved.
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In this paper a mathematical model is proposed, which incorporates quarantine and hospitalization to assess the community impact of social distancing and face mask among the susceptible population. The model parameters are estimated and fitted to the model with the use of laboratory confirmed COVID-19 cases in Turkey from March 11 to October 10, 2020. The partial rank correlation coefficient is employed to perform sensitivity analysis of the model, with basic reproduction number and infection attack rate as response functions. Results from the sensitivity analysis reveal that the most essential parameters for effective control of COVID-19 infection are recovery rate from quarantine individuals (delta(1)), recovery rate from hospitalized individuals (delta(4)), and transmission rate (beta). Some simulation results are obtained with the aid of mesh plots with respect to the basic reproductive number as a function of two different biological parameters randomly chosen from the model. Finally, numerical simulations on the dynamics of the model highlighted that infections from the compartments of each state variables decreases with time which causes an increase in susceptible individuals. This implies that avoiding contact with infected individuals by means of adequate awareness of social distancing and wearing face mask are vital to prevent or reduce the spread of COVID-19 infection.
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Aim: The world is affected by the severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) pandemic. This virus has emerged as a human pathogen that can cause symptoms ranging from fever to Pneumonia, but it remains asymptomatic or mild. To better understand the virus's ongoing spread, identify those who have been infected, and track the immune response, accurate and robust immunological monitoring and SARS-CoV-2 detection assays are needed. Methods: The estimation of serology tests to assess the presence of antibodies to SARS-CoV-2 in COVID-19 patients at Asian Institute of Medical Sciences (AIMS) and Isra University & hospital. 1229 patients were selected including males and females with the age being 25 to 65 years living in the territories from 1st August to 30th November 2020. The anti-SARS-CoV-2 test was performed by an electrochemiluminescence immunoassay analyzer. Results: Out of 1229 participants 206 (17%) were positive with COVID-19, and 1023 (83%) were negative. The results further revealed that a higher percentage of positive COVID-19 were detected in males in all age groups as compared to females, and most of them are affected at age of 46-65 years male 40 (24.69%) and female 14(17.5%). Conclusion: The seroprevalence of SARS-COV-2 antibodies has increased in the old age population, which may aid in determining the true number of infected cases. Although the current study is based on a small sample of participants, the findings suggest a study with a larger population to implement stronger and targeted interventions.
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Aim: To understand the psychological impact of COVID - 19 on Medical Students of a private sector Medical University in Karachi, Pakistan. Method: This cross-sectional study was conducted among medical students studying at Hamdard College of Medicine and Dentistry, Karachi, Pakistan. The data collection was done through online survey from July 2020 to December 2020. The study aimed to gather data from many medical students. A total number of 420 students were participated from Hamdard College of Medicine and Dentistry in Karachi, Pakistan. The participants were selected from all years of MBBS and BDS programs . Results: Out of 420 participants, 236 (56.2%) were male and 184 (43.8%) female, with a male:female ration of 1.28:1. Majority of participants were single as 411 (97.9%), of 224 (53.3%) students living with their family, 150 (35.7%) in hostel and 46 (11%) living with friends. In our sample 369 (87.9%) students studying in MBBS program while only 51 (12.1%) BDS, among those 80 (19%) medical students were in first year, followed by 122 (29%) second year, 65 (15.5%) third year, 54 (12.9%) fourth year and 99 (23.6%) studying in final year. IES-R scale and results shows 75 (17.9%) reported that PTSD is a clinical concern, probable diagnosis of PTSD 28 (6.7%) and majority rated as high enough to PTSD 133 (31.7%). Impact of event (revised) scale shows significant association with age and year of study with p value 0.026 and 0.002 respectively. Based on the PHQ9 scale, Gender, Living arrangements and the program enrolled in were reported significant association with depression p values 0.059, 0.008 and 0.006 respectively. Conclusion: Findings suggests high rate of anxiety, depression, and signs of PTSD in medical students due to COVID-19 which needs pressing attention and provision of professional help from mental health practitioners.
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Background: Covid-19 is a very contagious and quickly spreading viral infection, caused by a corona virus SARS-COV-2 which was originally reported in China on December 5, 2019. It was confirmed as pandemic by WHO on March 11, 2020. This disease is yet under research. It has variable severity which includes no symptoms to pneumonia. This can cause death of the patient. Aim: To evaluate the association of Lymphopenia with severity of COVID 19 in COVID-19 patients Methods: It was a retrospective observational study conducted in COVID wards of Ghurki hospital Lahore. Record of 100 COVID-19 patients that were admitted between March and July 2021 fulfilling the inclusion criteria was included in the study. A pre-structured pro forma was filled to collect the data. Results: Out of 100 patients, 30 patients were included in Non-severe group while severe group had 70 patients. The mean age of study population was 52.5±10.38 with 60% male and 40% female. 70% patients in severe group had some co-existent comorbidity. The most commonly reported symptoms were fever and cough in both groups while shortness of breath was more commonly reported in severe group. Conclusion: Lymphopenia is associated with severe Coronavirus disease 2019 (COVID-19) infections. Lymphocytes count can be used to assess the severity of COVID 19.
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Objective: To determine mental health problems such as anxiety, depression, and cognitive-behavioural changes in medical teachers due to a sudden rise in covid-19 cases along with a flood of social media traffic, mostly misinformation. Methodology: This cross-sectional study was conducted online from March to April 2021 and included 227 Medical teachers of HBS Medical College, IMDC, Rawal, and Federal Medical College, Islamabad. Those who were known cases of anxiety and depression were excluded from the study. We used online survey through Google docs. The Questionnaire inquired demographic information, info-media use and mental health functioning. A Chi-square test was applied to calculate the association of mental disorders with infodemics. Results: Out of 227 teachers, 64(28.19%) were male and 163(71.8%) females. They became easily annoyed or irritable (p=0.028) and felt afraid and had problems with appetite (p=0.001). The older age group, 66.1% significantly felt fearful (p=0.001) which was more in females 82.4% (p=0.001). Conclusion: Mental health problems due to the psychological impact of the Covid-19 pandemic were positively associated with the frequent use of info-media during the outbreak.