As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

In past 24 hours, Pakistan, Asia had N/A new cases, N/A deaths and N/A recoveries.

Dataset

This dataset was created by Shahraiz Ali

Released under CC0: Public Domain

Contents

This dataset demonstrates the fear of Coronavirus (COVID-19) among the people of Khyber Pakhtunkhwa (Pakistan), their preventive behaviour, mental health condition, and level of anxiety during the pandemic. To gauge these constructs, a questionnaire was developed with the help of various scales – Fear of COVID-19 Scale (FCV-19S), Positive Mental Health Scale (PMHS), and General Anxiety Disorder Scale (GAD). At the time of data collection, the COVID-19 cases were emerging rapidly in the country due to which the KPK province was also facing lock-down and other mobility restrictions to limit the spread of viral infection. Keeping in view the prevalent emergency conditions, the research tool was developed in Google form and disseminated online for the collection of data. The informed consent of the respondents was obtained electronically, and they participated voluntarily in this survey research. Social media apps like Facebook, WhatsApp, LinkedIn, and personal contacts were also used for speedy collection of data. All the questions in the questionnaire were mandatory and the respondents could not send their responses by skipping any of them, so there is no missing value in the dataset. A total of 501 responses were received out of which 208 were females. For the convenience of the participants, every question in the questionnaire was translated into the Urdu language. All the responses were automatically saved online into a .xlsx spreadsheet and later that data was converted to digitized form by developing a coding frame. There are two main sections in this dataset, first is about the socio-demographic information (gender, age, marital status, employment status, area of residence and education) of the participants and the second demonstrates the fear, mental health, preventive behaviour, and anxiety while in the second section, the responses were rated on Likert scale. This dataset could be beneficial to the researchers and policymakers as they can get further insight to develop better skills and practices from a rapidly evolving situation.

COVID-19 Confirmed Cases by Division and District, The Punjab

As of April 13, 2024, India had the highest number of confirmed deaths due to the outbreak of the novel coronavirus in the Asia-Pacific region, with over 533 thousand deaths. Comparatively, Indonesia, which had the second highest number of coronavirus deaths in the Asia-Pacific region, recorded approximately 162 thousand COVID-19 related deaths as of April 13, 2024. Contrastingly, Bhutan had reported 21 deaths due to COVID-19 as of April 13, 2024.

Accuracy of different time series models for cumulative confirmed cases.

The data consist of COVID-19 cases and its relevant parameters for a few countries including: Pakistan, Bangladesh, India and Afghanistan on daily basis from December 31, 2019 to August 19, 2020 acquired from https://ourworldindata.org/coronavirus-source-data.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

----------------------UPDATED------UPDATED---------UPDATED----------------------- ----------------------------- (3616 COVID-19 Chest X-ray) -------------------------------

COVID-19 RADIOGRAPHY DATABASE (Winner of the COVID-19 Dataset Award by Kaggle Community)

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

Please find the link for downloading the whole dataset: Data

Please cite the following two articles if you are using this dataset:

-M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Paper link -Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. Paper Link

To view images please check image folders and references of each image are provided in the metadata.xlsx.

*****Research Team members and their affiliation***** Muhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Tawsifur Rahman (tawsifurrahman.1426@gmail.com) Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Amith Khandakar (amitk@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Rashid Mazhar, MD Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar Muhammad Abdul Kadir, PhD Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Zaid Bin Mahbub, PHD Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh Khandakar R. Islam, MD Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh Muhammad Salman Khan, PhD Department of Electrical Engineering (JC), University of Engineering and Technology, Peshawar-25120, Pakistan Prof. Atif Iqbal, PhD Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Nasser Al-Emadi, PhD Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Prof. Mamun Bin Ibne Reaz. PhD Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia

****Contribution**** - We have developed the database of COVID-19 x-ray images from the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 DATABASE [1], Novel Corona Virus 2019 Dataset developed by Joseph Paul Cohen and Paul Morrison, and Lan Dao in GitHub [2] and images extracted from 43 different publications. References of each image are provided in the metadata. Normal and Viral pneumonia images were adopted from the Chest X-Ray Images (pneumonia) database [3].

Image Formats - All the images are in Portable Network Graphics (PNG) file format and the resolution are 299*299 pixels.

Objective - Researchers can use this database to produce useful and impactful scholarly work on COVID-19, which can help in tackling this pandemic.

Citation - Please cite these papers if you are using it for any scientific purpose: -M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Paper link -Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. Paper Link

Acknowledgments Thanks to the Italian Society of Medical and Interventional Radiology (SIRM) for publicly providing the COVID-19 Chest X-Ray dataset [3], Valencia Region Image Bank (BIMCV) padchest dataset [1] and would like to thank J. P. Cohen for taking the initiative to gather images from articles and online resources [5]. Finally to the Chest X-Ray Images (pneumonia) database in Kaggle and Radiological Society of North America (RSNA) Kaggle database for making a wonderful X-ray database for normal, lung opacity, viral, and bacterial pneumonia images [8-9]. Also, a big thanks to our collaborators! DATA ACCESS AND USE: Academic/Non-Commercial Use References: [1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711 [2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png [3]https://sirm.org/category/senza-categoria/covid-19/ [4]https://eurorad.org [5]https://github.com/ieee8023/covid-chestxray-dataset [6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328 [7]https://github.com/armiro/COVID-CXNet
[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

Spillover effect of COVID-19 cases on stock market return volatility.

Authors of the Dataset:

Pratik Bhowal (B.E., Dept of Electronics and Instrumentation Engineering, Jadavpur University Kolkata, India) [LinkedIn], [Github] Subhankar Sen (B.Tech, Dept of Computer Science Engineering, Manipal University Jaipur, India) [LinkedIn], [Github], [Google Scholar] Jin Hee Yoon (faculty of the Dept. of Mathematics and Statistics at Sejong University, Seoul, South Korea) [LinkedIn], [Google Scholar] Zong Woo Geem (faculty of College of IT Convergence at Gachon University, South Korea) [LinkedIn], [Google Scholar] Ram Sarkar( Professor at Dept. of Computer Science Engineering, Jadavpur Univeristy Kolkata, India) [LinkedIn], [Google Scholar]

Overview The authors have created a new dataset known as Novel COVID-19 Chestxray Repository by the fusion of publicly available chest-xray image repositories. In creating this combined dataset, three different datasets obtained from the Github and Kaggle databases,created by the authors of other research studies in this field, were utilized.In our study,frontal and lateral chest X-ray images are used since this view of radiography is widely used by radiologist in clinical diagnosis.In the following section, authors have summarized how this dataset is created.

COVID-19 Radiography Database: The first release of this dataset reports 219 COVID-19,1345 viral pneumonia and 1341 normal radiographic chest X-ray images. This dataset was created by a team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh in collaboration with medical doctors and specialists from Pakistan and Malaysia.This database is regularly updated with the emergence of new cases of COVID-19 patients worldwide.Related Paper:https://arxiv.org/abs/2003.13145

COVID-Chestxray set:Joseph Paul Cohen and Paul Morrison and Lan Dao have created a public image repository on Github which consists both CT scans and digital chest x-rays.The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Children’s medical center.With the aid of metadata information provided along with the dataset,we were able to extract 521 COVID-19 positive,239 viral and bacterial pneumonias;which are of the following three broad categories:Middle East Respiratory Syndrome (MERS),Severe Acute Respiratory Syndrome (SARS), and Acute Respiratory Distress syndrome (ARDS);and 218 normal radiographic chest X-ray images of varying image resolutions. Related Paper: https://arxiv.org/abs/2006.11988

Actualmed COVID chestxray dataset:Actualmed-COVID-chestxray-dataset comprises of 12 COVID-19 positive and 80 normal radiographic chest x-ray images.

The combined dataset includes chest X-ray images of COVID-19,Pneumonia and Normal (healthy) classes, with a total of 752, 1584, and 1639 images respectively. Information about the Novel COVID-19 Chestxray Database and its parent image repositories is provided in Table 1.

Table 1: Dataset Description | Dataset| COVID-19 |Pneumonia | Normal | | ------------- | ------------- | ------------- | -------------| | COVID Chestxray set | 521 |239|218| | COVID-19 Radiography Database(first release) | 219 |1345|1341| | Actualmed COVID chestxray dataset| 12 |0|80| | Total|752|1584|1639|

DATA ACCESS AND USE: Academic/Non-Commercial Use Dataset License : Database: Open Database, Contents: Database Contents

Chest X-ray images were selected from a database of chest X-ray images for COVID-19 positive cases along with normal and viral pneumonia images which were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. In their current release, there are 219 COVID-19 positive images, 1341 normal images and 1345 viral pneumonia images (Chowdhury et al., 2020). To ensure multiple representations, the dataset of chest X-ray images for both COVID-19 and normal cases were also selected from Mendeley dataset repository (El-Shafai, 2020) which contains 5500 Non-COVID X-ray images and 4044 COVID-19 X-ray images. This study, therefore, adopted these multi source datasets. Due to limited computing resources, in this study, 1,300 images were selected from each category. That is, 1,300 images of COVID-19 positive cases, 1,300 Normal images and 1,300 images of viral pneumonia cases, totaling 3,900 images in all. It is noted here that further descriptions of the datasets were not provided by the authors of the sources of the datasets.El-Shafai, Walid; Abd El-Samie, Fathi (2020), “Extensive COVID-19 X-Ray and CT Chest Images Dataset”, Mendeley Data, V3, doi: 10.17632/8h65ywd2jr.3Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., . . . Reaz, M. B. (2020, March 29). Can AI help in screening Viral and COVID-19 pneumonia? Retrieved from https://arxiv.org/abs/2003.13145; https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

As of March 20, 2023, over 13 billion COVID-19 vaccine doses had been administered worldwide, with the United States accounting for almost 672 million of this total. This statistic shows the number of COVID-19 vaccine doses administered worldwide as of March 20, 2023, by country.

IntroductionThe mental health of South Asian women has been observed to be in regression lately, with sexual harassment as one of the major factors accounting for mental health deterioration, especially for women who leave their homes frequently for work and study. The COVID-19 pandemic not only augmented the mental health distress of the general female population but the rise in sexual violence against women is being consistently reported around the globe. Based on this background, we adopted a two-pronged strategy to assess whether working women and students aged 18–55 experienced a rise in sexual harassment in the 18 months after lifting the COVID-19 lockdowns. Secondly, using the well-validated psychometric test, DASS-21, we evaluated the psychiatric outcome of this change on the mental health of those women.Study designThe study was designed as a quantitative, cross-sectional survey-based research.MethodologyA total of 303 women participated in this study. Personal interviews through a specifically designed questionnaire and psychometric test DASS-21 were administered to assess the mental health state of working women and female students, aged between 18 and 55 years old. The mean age of the participants was 37 ± 2.8. The study population was further categorized into two main groups of limited and frequent interactions based on varying levels of the frequency of leaving home and interacting with male strangers in their daily routine. Data were analyzed and the correlation between limited/frequent interaction and DASS-21 total scores and sub-scores of depression, anxiety and stress, and other sociodemographic variables were investigated using the Chi-square test, whereas psychosocial predictors of mental distress were evaluated using multiple linear regression analysis after matching limited and frequent interaction groups using a 1:1 propensity score-matched pair method for sociodemographic covariates.ResultsOverall, approximately 50% of our study population experienced changes in the behavior of male strangers that could be categorized as harassment in their daily life interactions, whereas 33.66% of participants experienced relatively more sexual harassment post-pandemic than before it. This observation was significantly correlated with the frequency of male interaction (χ2 = 5.71, p 60 on the DASS21-total score, whereas 29.04% scored >21 on the depression scale. Alarmingly, >40% of the women in the frequent interaction group scored in the extremely severe range of anxiety and depression. Moreover, in the regression analysis, out of all the factors analyzed, the extent of everyday interaction with male strangers, an increase in fear of sexual crimes, and a self-perceived increase in mental distress during the 18 months post-pandemic were found to be highly statistically significant predictors of mental distress not only for total DASS 21 but also for the sub-scales of depression, anxiety, and stress.ConclusionIn Pakistan, women experienced a rise in sexual harassment cases post–COVID–19. An increase in sexual harassment was found to be a predictor of negative mental health in the form of depression, anxiety, and stress.

COVID-19 survivors’ return to work and HADS score stratified according to vaccination status.