Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 tests, cases, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported daily, with

This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist. To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark.

In particular, we focus on learning to segment left lung, right lung, and infections using

pure but limited COVID-19 CT scans;

existing labeled lung CT dataset from other non-COVID-19 lung diseases;

heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.

This public use dataset has 11 data elements reflecting COVID-19 community levels for all available counties. This dataset contains the same values used to display information available at https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels-county-map.html. CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium , or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information. Visit CDC’s COVID Data Tracker County View* to learn more about the individual metrics used for CDC’s COVID-19 community level in your county. Please note that county-level data are not available for territories. Go to https://covid.cdc.gov/covid-data-tracker/#county-view.

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.

Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.

Data Abstract

1. 120 de-identified Thoracic CT scans from COVID negative patients.

2. Supporting clinical variables: MRN*, Age, Exam Date/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

***This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.

Related Links Dataset https://www.kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets is related to this dataset Dataset https://github.com/ieee8023/covid-chestxray-dataset is related to this dataset Dataset http://dx.doi.org/10.17632/2fxz4px6d8.4 is related to this dataset Dataset https://github.com/UCSD-AI4H/COVID-CT is related to this dataset

This dataset provides the following measures related to COVID-19 in CT public and private PK-12 schools for the latest week-long reporting period:

Number of staff cases and change from the previous reporting period Number of student cases and change from the previous reporting period Number of student cases by learning model (fully in-person, hybrid, fully remote, or unknown) and change from the previous reporting period

As of 6/24/2021, COVID-19 school-based surveillance activities for the 2020 – 2021 academic year has ended. The Connecticut Department of Public Health along with the Connecticut State Department of Education are planning to resume these activities at the start of the 2021 – 2022 academic year.

Data for the 2021-2022 school year is available here: https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-in-CT-Schools-State-Summary-2021-20/r6vy-dvtz

however

A CT scan dataset about COVID-19

This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by Revel, M. et al (2021). Data was restructured in compliance with the AUC23 challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the STOIC2021 challenge.

Images are 3D tensors:

• 0: 3D CT scan

Classification labels:

• COVID-19:
  • 0: Negative RT-PCR
  • 1: Positive RT-PCR
    
• Severe COVID-19:
  • 0: Alive and no intubation after one month
  • 1: Death or intubation after one month

imagesTr (root folder with all patients and studies)
├── covid19severity_6_0000.mha (3D CT for study 6)
├── covid19severity_17_0000.mha (3D CT for study 17)
├── ...

Please cite the following article if you are using the STOIC2021 training dataset:

STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.

Due to upload size limits, the data set was split into six batches.

Batch 1: https://zenodo.org/record/7969800

Batch 2: https://zenodo.org/record/8042589

Batch 3: https://zenodo.org/record/8042817

Batch 4: https://zenodo.org/record/8043089

Batch 6: https://zenodo.org/record/8043218

June 8, 2023: Daily transmission is no longer available. Summary of COVID-19 statistics for Connecticut correctional facilities including: Total # of Staff Positive for COVID-19 Total # of Inmates Pos. for COVID-19 COVID-19 Pos. Inmates Housed at Northern CI Medical Isolation Unit COVID-19 Pos. Inmates Housed at MacDougall-Walker Medical Isolation Unit COVID-19 Pos. Staff Returned to Work Total # of Inmates Medically Cleared Total # of COVID-19 Pos. Inmate Deaths More information can be found on the DOC website: https://portal.ct.gov/DOC/Common-Elements/Common-Elements/Health-Information-and-Advisories Data will be updated every weekday. Additional notes: The data on 7/15 reflects a decrease in the number of inmates testing positive for COVID-19 and those who have recovered; this decrease was due to an internal data audit that led to the removal of some duplicate information. The data on 6/2/2020 reflects an increase in the number of inmates who had been medically cleared; this increase was the result of 146 asymptomatic positive inmates who had completed a 14-day isolation period.

Waleed-bin-Qamar/covid-dataset-CT-images dataset hosted on Hugging Face and contributed by the HF Datasets community

The images are collected from COVID19-related papers from medRxiv, bioRxiv, NEJM, JAMA, Lancet, etc. CTs containing COVID-19 abnormalities are selected by reading the figure captions in the papers. All copyrights of the data belong to the authors and publishers of these papers. For more information about the dataset, find the following article on arxiv and the data&code at GitHub.

Context

Abstract from the pre-print of the authors : CT scans are promising in providing accurate, fast, and cheap screening and testing of COVID-19. In this paper, we build a publicly available COVID-CT dataset, containing 275 CT scans that are positive for COVID-19, to foster the research and development of deep learning methods which predict whether a person is affected with COVID-19 by analyzing his/her CTs. We train a deep convolutional neural network on this dataset and achieve an F1 of 0.85 which is a promising performance but yet to be further improved. The data and code are available at https://github.com/UCSD-AI4H/COVID-CT

Inspiration

This dataset can be used to perform classification and automatically detect COVID-19 on CT scans

We built a large lung CT scan dataset for COVID-19 by curating data from 7 public datasets. Three of these datasets had shared COVID-19 lesion masks. This dataset merges the COVID-19 lesion masks and their corresponding frames of these 3 public datasets, with 2729 image and ground truth mask pairs. All different types of lesions are mapped to white color for consistency across datasets.

Acknowledgements

• S. Morozov et al., "MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset," arXiv preprint arXiv:2005.06465, 2020.
• M. Jun et al., "COVID-19 CT Lung and Infection Segmentation Dataset," Zenodo, Apr, vol. 20, 2020.
• "COVID-19." 2020. [Online] http://medicalsegmentation.com/covid19/ [Accessed 23 December, 2020].

NOTE: As of 2/16/2023, this table is not being updated. For data on COVID-19 updated (bivalent) booster coverage by town please to go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Town/bqd5-4jgh. This table shows the number and percent of residents of each CT town that have initiated COVID-19 vaccination, are fully vaccinated and who have received additional dose 1. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose. A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. Percentages are calculated using 2019 census data (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-Town-and-County-Population-for-Connecticut). Town of residence is verified by geocoding the reported address and then mapping it to a town using municipal boundaries. If an address cannot be geocoded, the reported town is used, if available. People for whom an address is not currently available are shown in this table as “Address pending validation”. Out-of-state residents vaccinated by CT providers are excluded from the table. Town-level coverage estimates have been capped at 100%. Observed coverage may be greater than 100% for multiple reasons, including census denominator data not including all individuals that currently reside in the town (e.g., part time residents, change in population size since the census), errors in address data or other reporting errors. Also, the percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated when records for an individual cannot be linked because of differences in how names or date of birth are reported. Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while studying remotely in his/her hometown, the student may be counted as a vaccine recipient in that town. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, ho

NOTE: there is no peer-reviewed publication associated with this data record.This fileset consists of three datasets in .xlsx file format.Dataset CLIN LAB DATA RAD-Covid (1).xlsx contains the patients’ demographic data, comorbidities, and outcome (death or recovery), collected from the institution’s electronic medical records. Additionally, the file contains clinical severity of COVID-19, upon hospital admission. This was classified according to the institution’s treatment protocol for patients with suspected Covid-19: mild (home treatment), moderate (hospitalization), or severe (intensive care unit [ICU] admission).Dataset consensus RADIOLOGISTS CT AVAL. PATTERNS AND DISTRIBUTION OF LESIONS (1).xlsx contains the chest CT imaging findings (i.e the radiological patterns and distribution of lesions).Dataset RAD-COVID SCORE AGREEMENT (1).xlsx contains the radiological severity score (RAD-Covid Score) that was assigned to the CT scan of each patient.The scores were assigned by two radiologists, at independent workstations, and the results are shown in spreadsheets “Radiologist 1” and “Radiologist 2”, respectively. The percentage values next to each RAD-Covid Score represent pulmonary involvement.Study aims and methodology: The severity of pulmonary Covid-19 infection can be assessed by the pattern and extent of parenchymal involvement observed in computed tomography (CT), and it is important to standardize the analysis through objective, practical, and reproducible systems.In this study, the authors propose a method for stratifying the radiological severity of pulmonary disease, the Radiological Severity Score (RAD-Covid Score), in Covid-19 patients by quantifying infiltrate in chest CT, including assessment of its accuracy in predicting disease severity.The study was approved by the institutional research ethics committee, although the consent requirement was waived due to its retrospective nature.Institutional Review Board approval was obtained from Dante Pazzanese Cardiology Institute Ethical Committee CAAE: 32408920.2.0000.5462.A total of 658 patients were included in the study. Only patients (a) whose Covid-19 infection was confirmed by real-time polymerase chain reaction and (b) who underwent chest CT on admission between March 6 and April 6, 2020 were included. Patients (a) whose real-time polymerase chain reaction examinations were performed more than 7 days after chest CT and (b) who were under 18 years of age were excluded.The patients’ demographic data (age, gender), comorbidities, and outcome (death or recovery) were collected from the institution’s electronic medical records. Clinical severity upon hospital admission was classified according to the institution’s treatment protocol for patients with suspected Covid-19: mild (home treatment), moderate (hospitalization), or severe (intensive care unit [ICU] admission).Chest CT scans were obtained through low-radiation-dose on a 160-MDCT (Aquilion Prime CT, Toshiba/Canon), 64-MDCT (Optma 660, GE), 16-MDCT (Somaton Scope,Siemens), 16-MDCT (Alexion, Toshiba/Canon) and 16-MDCT (BrightSpeed, GE Heathcare). Two radiologists, both with 8 years’ experience in chest imaging and blinded to the clinical and laboratory data, performed a standardized review of all chest CT images at independent workstations.For more details on the methodology and statistical analysis, please read the related article.

About this dataset

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. In total, there are 1000 CT scans each from a unique patient.

A subset of 50 studies has been annotated with binary pixel masks for segmentation depicting regions of interest (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by medical hospitals in Moscow, Russia.

How to use this dataset

Related COVID-19 CT dataset (different source) For more datasets, click here.

How to cite this dataset

If you use this dataset in your research, please credit the authors

Citation

Morozov, S., Andreychenko, A., Blokhin, I., Vladzymyrskyy, A., Gelezhe, P., Gombolevskiy, V., Gonchar, A., Ledikhova, N., Pavlov, N., Chernina, V. MosMedData: Chest CT Scans with COVID-19 Related Findings, 2020, v. 1.0, link

License

CC BY NC ND 3.0

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Image by rawpixel, available here.

We describe a publicly available multiclass CT scan dataset for SARS-CoV-2 infection identification. Which currently contains 4173 CT-scans of 210 different patients, out of which 2168 correspond to 80 patients infected with SARS-CoV-2 and confirmed by RT-PCR. These data have been collected in the Public Hospital of the Government Employees of Sao Paulo (HSPM) and the Metropolitan Hospital of Lapa, both in Sao Paulo - Brazil. The dataset is composed of CT scans in png format, which are divided into: 758 CT scans for healthy patients (15 CT scans per patient on average). 2168 CT scans for patients infected by SASR-CoV-2(27 CT scans per patient on average). 1247 CT scans for patients with other pulmonary directions (16 CT scans per patient on average). TOTAL: 4173 CT scans for 210 patients of Sao Paulo - Brazil (20 CT scans per patient on average).

pathologies

COVID-CT dataset that has been used in this study is publicly available. There are 349 images of COVID-19 collected from 216 patients. The non-COVID-19 data contains 397 samples.

This dataset includes CT data and segmentation masks from patients diagnosed with COVID-19, as well as data from subjects without the infection.

This study is approved under the ethical approval codes of IR.TUMS.IKHC.REC.1399.255 and IR.TUMS.VCR.REC.1399.488 at Tehran University of Medical Sciences.

The code for loading the dataset and running an AI model is available on: https://github.com/SamanSotoudeh/COVID19-segmentation

Please use the following citations:

1- Arian, Arvin; Mehrabinejad, Mohammad-Mehdi; Zoorpaikar, Mostafa; Hasanzadeh, Navid; Sotoudeh-Paima, Saman; Kolahi, Shahriar; Gity, Masoumeh; Soltanian-Zadeh, "Accuracy of Artificial Intelligence CT Quantification in Predicting COVID-19 Subjects’ Prognosis" PLoS ONE (2023).

2- Sotoudeh-Paima, Saman, et al. "A Multi-centric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans." Iranian Journal of Radiology 19.4 (2022).

3- Hasanzadeh, Navid, et al. "Segmentation of COVID-19 Infections on CT: Comparison of four UNet-based networks." 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME). IEEE, 2020.