Bangladesh recorded 29223 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Bangladesh reported 2038539 Coronavirus Cases. This dataset includes a chart with historical data for Bangladesh Coronavirus Deaths.

Dataset

This dataset was created by Md Mahmud Ferdous

Contents

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Background COVID-19 is a highly transmittable respiratory illness induced by SARS-CoV-2, a novel coronavirus. The spatio-temporal analysis considers interactions between space and time is essential for understanding the virus’s transmission pattern and developing efficient mitigation strategies. Objective This study explicitly examines how meteorological, demographic, and vaccination with all doses of risk factors are interrelated with COVID-19’s complex evolution and dynamics in 64 Bangladeshi districts over space and time. Methods The study employed Bayesian spatio-temporal Poisson modeling to determine the most suitable model, including linear trend, analysis of variance (ANOVA), separable models, and Poisson temporal model for spatiotemporal effects. The study employed the Deviance Information Criterion (DIC) and Watanabe-Akaike information criterion (WAIC) for model selection. The Markov Chain Monte Carlo approach also provided information regarding both prior and posterior realizations. Results The results of our study indicate that the spatio-temporal Poisson ANOVA model outperformed all other models when considering various criteria for model selection and validation. This finding underscores the significant relationship between spatial and temporal variations and the number of cases. Additionally, our analysis reveals that maximum temperature does not appear to have a significant association with infected cases. On the other hand, factors such as humidity (%), population density, urban population, aging index, literacy rate (%), households with internet users (%), and complete vaccination coverage all play vital roles in correlating with the number of affected cases in Bangladesh. Conclusions The research has demonstrated that demographic, meteorological, and vaccination variables possess significant potential to be associated with COVID-19-affected cases in Bangladesh. These data show that there are interconnections between space and time, which shows how important it is to use integrated modeling in pandemic management. An assessment of the risks particular to an area allows government agencies and communities to concentrate their efforts to mitigate those risks.

The Rohingya population settled in 34 camps in Cox's Bazar district numbers around 860,000 individuals.1 On March 23, 2020, Cox's Bazar saw the first case of COVID-19 in the host community. The first case of COVID19 in the Rohingya population was confirmed on 14 May 2020. 2 At the time of this survey (September 5th - 10th 2020), there were 138 confirmed cases of, and 8 confirmed deaths from COVID19 in the Rohingya camps.3 Community engagement around prevention of COVID19 has been a core activity of the health sector since March and is supported by many other sectors. Activities are carried out by volunteers through door-door messaging and the use of multimedia approaches, key messages were developed by the risk communication group including the need for physical distancing, mask wearing, recognising symptoms and testing and treatment. The assessment was designed to assess the effectiveness of the intense community engagement that has been done among the Rohingya population;whether people were absorbing and developing good knowledge from the communication and informationoutreach, and whether they were responding (through behaviour change) to the information they were receiving.

This dataset compiles the primary data and related statistical analyses for assessing the impact of COVID-19 on the high-value crop farmers in Bangladesh.

Objective: Coronavirus disease 2019 (COVID-19) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global issue. In addition to managing acute cases, post-COVID-19 persisting symptoms/complaints and different hematological values are of great concern. These have an impact on the patient's well-being and are yet to be evaluated. Therefore, clinical and primary diagnosis based on routine laboratory findings bears high importance during the initial period of COVID-19, especially in regions with fewer diagnostic facilities.Methods: Clinical information and associated complaints of the COVID-19 illness confirmed by reverse transcription-polymerase chain reaction (RT-PCR) were collected directly from the patients. Regular follow-ups were obtained on the phone every 2 weeks following recovery for 20 weeks. Initial hematological and radiology findings of the hospitalized patients except for intensive care unit (ICU) and high dependency units (HDUs) and a follow-up evaluation after 4 weeks following recovery were analyzed.Results: The post-COVID-19 persisting symptoms/complaints were found among 21.4% of symptomatic patients, which persisted for ≥20 weeks and had a significant relationship with the duration of COVID-19 illness and the existing comorbidity (p < 0.05). Post-COVID-19 primary type 2 diabetes mellitus (DM, 0.64%) and hypertension (HTN, 1.28%) and unstable DM (54.55%) and HTN (34.78%) to the pre-existing diabetic and hypertensive patients were observed. Post-recovery remarkable changes in the laboratory values included leukocytosis (16.1%), lymphocytosis (14.5%), and an increased prothrombin time (PT, 25.8%). Abnormalities in the D-dimer, serum ferritin, hemoglobin, and erythrocyte sedimentation rate (ESR) levels were present to an extent. Laboratory findings like chest X-ray, ESR, white blood cell (WBC) count, lymphocyte count, C-reactive protein (CRP), serum glutamic pyruvic transaminase (SGPT), serum ferritin, PT, D-dimer, and serum creatinine are important markers for the diagnosis and prognosis of COVID-19 illness (p < 0.05).Conclusion: Post-COVID-19 persisting symptoms and the changes in the laboratory values need to be considered with importance and as a routine clinical measure. Post-COVID-19 periodic follow-up for evaluating the patient's physical condition and the biochemical values should be scheduled with care and managed accordingly to prevent future comorbidity in patients with the post-COVID-19 syndrome.

The ongoing COVID-19 pandemic has been a scientific, medical and social challenge. Since clinical course of this disease is largely unpredictable and can develop rapidly causing severe complications, it is important to identify laboratory biomarkers, which may help to classify patient’s severity during initial stage. Previous studies have suggested C—reactive protein (inflammatory) and D-dimer (biochemical) as an effective biomarker. The differential severity in patients across the world and our limited understanding in the progression of the disease calls for a multi-country analysis for biomarkers. Therefore, we have analyzed these biomarkers among 228 Bangladeshi COVID-19 patients. We observed significant association of COVID-19 severity with these two biomarkers. Thus, we suggest to use these biomarkers for Bangladeshi COVID-19 patients for better disease monitoring. Such validated preventive measures may decrease the case fatality ratio substantially.

Background: Post-coronavirus disease (COVID-19) syndrome includes persistence of symptoms beyond viral clearance and fresh development of symptoms or exaggeration of chronic diseases within a month after initial clinical and virological cure of the disease with a viral etiology. We aimed to determine the incidence, association, and risk factors associated with development of the post-COVID-19 syndrome.

Methods: We conducted a prospective cohort study at Dhaka Medical College Hospital between June 01, 2020 and August 10, 2020. All the enrolled patients were followed up for a month after clinical improvement, which was defined according the World Health Organization and Bangladesh guidelines as normal body temperature for successive 3 days, significant improvement in respiratory symptoms (respiratory rate <25/breath/minute with no dyspnea), and oxygen saturation >93% without assisted oxygen inhalation.

Findings: Among the 400 recruited patients, 355 patients were analyzed. In total, 46% patients developed post-COVID-19 symptoms, with post-viral fatigue being the most prevalent symptom in 70% cases. The post-COVID-19 syndrome was associated with female gender (relative risk [RR]: 1.2, 95% confidence interval [CI]: 1.02–1.48, p=0.03), those who required a prolonged time for clinical improvement (p<0.001), and those showing COVID-19 positivity after 14 days (RR: 1.09, 95% CI: 1.00–1.19, p<0.001) of initial positivity. Patients with severe COVID-19 at presentation developed post-COVID-19 syndrome (p=0.02). Patients with fever (RR: 1.5, 95% CI: 1.05–2.27, p=0.03), cough (RR: 1.36, 95% CI: 1.02–1.81, p=0.04), respiratory distress (RR: 1.3, 95% CI: 1.4–1.56, p=0.001), and lethargy (RR: 1.2, 95% CI: 1.06–1.35, p=0.003) as the presenting features were associated with the development of the more susceptible to develop post COVID-19 syndrome than the others. Logistic regression analysis revealed female sex, respiratory distress, lethargy, and long duration of the disease as risk factors.

Conclusion: Female sex, respiratory distress, lethargy, and long disease duration are critical risk factors for the development of post-COVID-19 syndrome.

----------------------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

Evaluation of parameters for the XGBoost models of different training and test sets for COVID-19 deaths.

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

Distribution of patient characteristics and co-morbidities in COVID-19 cases.

Summary of COVID-19 confirmed cases and deaths count during March 08- November 30, 2021.

Basic and clinical characteristics of the patients with COVID-19 (N = 600).

Associations between the variants detected by RT-PCR and the presence or absence of COVID-19 symptoms.