NOTE: This layer is deprecated (last updated 1/11/2022). Was formerly a daily update.

Summary

The 7-day average percent positive rate for COVID-19 tests adminstered among Marylanders under 35 years of age and over 35 years of age.

Description

Testing volume data represent the static daily total of PCR COVID-19 tests electronically reported for Maryland residents; this count does not include test results submitted by labs and other clinical facilities through non-electronic means. The 7-day percent postive rate is a rolling average of each day’s positivity percentage. The percentage is calculated using the total number of tests electronically reported to MDH (by date of report) and the number of positive tests electronically reported to MDH (by date of report). Electronic lab reports from NEDDSS.

Terms of Use

The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

COVID-QU-Ex Dataset

The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including: - 11,956 COVID-19 - 11,263 Non-COVID infections (Viral or Bacterial Pneumonia) - 10,701 Normal Ground-truth lung segmentation masks are provided for the entire dataset. This is the largest ever created lung mask dataset.

If you would like to download the COVID-QU-Ex dataset, then please complete the following form: https://docs.google.com/forms/d/e/1FAIpQLScRc10ZYhk57CNAY48q5EYOBKa8QU3uastvN1yavmf8zfB6vw/viewform?usp=sf_link

If you use COVID-QU-Ex Dataset in your research, please consider to cite the publications/dataset below: [1] A. M. Tahir, M. E. H. Chowdhury, A. Khandakar, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Madeed, S. Mahmud, M. Ezeddin, K. Hameed, and T. Hamid, “COVID-19 Infection Localization and Severity Grading from Chest X-ray Images”, Computers in Biology and Medicine, vol. 139, p. 105002, 2021, https://doi.org/10.1016/j.compbiomed.2021.105002. [2] Anas M. Tahir, Muhammad E. H. Chowdhury, Yazan Qiblawey, Amith Khandakar, Tawsifur Rahman, Serkan Kiranyaz, Uzair Khurshid, Nabil Ibtehaz, Sakib Mahmud, and Maymouna Ezeddin, “COVID-QU-Ex .” Kaggle, 2021, https://doi.org/10.34740/KAGGLE/DSV/2759090. [3] T. Rahman, A. Khandakar, Y. Qiblawey A. Tahir S. Kiranyaz, S. Abul Kashem, M. Islam, S. Al Maadeed, S. Zughaier, M. Khan, M. Chowdhury, "Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images," Computers in Biology and Medicine, p. 104319, 2021, https://doi.org/10.1016/j.compbiomed.2021.104319. [4] A. Degerli, M. Ahishali, M. Yamac, S. Kiranyaz, M. E. H. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, "Covid-19 infection map generation and detection from chest X-ray images," Health Inf Sci Syst 9, 15 (2021), https://doi.org/10.1007/s13755-021-00146-8. [5] 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. A. Emadi, M. B. I. Reaz, M. T. Islam, "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665-132676, 2020, https://doi.org/10.1109/ACCESS.2020.3010287.

To the best of our knowledge, this is the first study that utilizes both lung and infection segmentation to detect, localize and quantify COVID-19 infection from X-ray images. Therefore, it can assist the medical doctors to better diagnose the severity of COVID-19 pneumonia and follow up the progression of the disease easily.

The experiments were conducted on two CXR sets, where each set is divided into train, validation and test sets: 1) Lung Segmentation Data Entire COVID-QU-Ex dataset (33,920 CXR images with corresponding ground-truth lung masks) 2) COVID-19 Infection Segmentation Data A subset of COVID-QU-Ex dataset (1,456 Normal and 1,457 Non-COVID-19 CXRs with corresponding lung mask, plus 2,913 COVID-19 CXRs with corresponding lung mask from COVID-QU-Ex dataset and corresponding infections masks from QaTaCov19 dataset).

References

In COVID-QU-Ex, the X-ray images are collected from the following repositories and studies: • COVID-19 Samples: [1- 7]. • Non-COVID Samples: [8- 10]. • Normal Samples: [8- 10].

[1] QaTa-COV19 Database. https://www.kaggle.com/aysendegerli/qatacov19-dataset. Accessed 14 March 2021. [2] Covid-19-image-repository. Available: https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png. Accessed 14 March 2021. [3] Eurorad. Available: https://www.eurorad.org/. Accessed 14 March 2021. [4] Covid-chestxray-dataset. Available: https://github.com/ieee8023/covid-chestxray-dataset. Accessed 14 March 2021. [5] COVID-19 DATABASE. Available: https://www.sirm.org/category/senza-categoria/covid-19/. Accessed 14 March 2021. [6] Kaggle. (2020). COVID-19 Radiography Database. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. Accessed 14 March 2021. [7] GitHub. (2020). COVID-CXNet. Available: https://github.com/armiro/COVID-CXNet. Accessed 14 March 2021. [8] RSNA Pneumonia Detection Challenge. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. Accessed 14 March 2021. [9] Chest X-Ray Images (Pneumonia). Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 14 March 2021. [10] Medical Imaging Databank of the Valencia Region. PadChest: A large chest x-ray image dataset with multi-label annotated reports. Available: https://bimcv.cipf.es/bimcv-projects/padchest/. Accessed 14 March 2021.

Background: This longitudinal study (April 2020 - January 2023) investigated lung function (spirometry, pulmonary diffusing capacity) in Mexican Hispanics who experienced severe COVID-19. It focused on how recovery time affects lung function improvements, hypothesizing that patients with a longer recovery between diagnosis and pulmonary testing would show better lung function than those tested earlier.

Methods: At a COVID-19 follow-up clinic in Yucatan, Mexico, lung function and symptoms were assessed in patients recovered from severe COVID-19. We used z-scores and Wilcoxon signed rank tests to analyze lung function changes over time. Lung function was measured twice in 82 patients: at a median of 94 and 362 days after COVID-19 diagnosis. High-resolution computed tomography (HRCT) was conducted in 44 of these 82 subjects, with a median time of 38 days between CT scanning and a pulmonary function test. Z-scores were determined using reference equations for spirometry (doi: 10.1164/rccm.202205-0963OC) and DLCO [Gochicoa-Rangel L. G. et al., (2024). Reference equations for DLNO & DLCO in Mexican Hispanics: Influence of altitude and race. BMJ Open Respir Res, in press as of September 2024].

Seven pulmonary ailments were assessed, identified based on the 2022 ERS/ATS interpretation strategies (doi: 10.1183/13993003.01499-2021): (A) Restrictive spirometry pattern (FEV1/FVC > LLN, FVC < LLN); (B) Airflow obstruction (FEV1/FVC < LLN, FVC > LLN); (C) Mixed disorder (FEV1/FVC < LLN, FVC < LLN); (D) Loss of alveolar-capillary structure with loss of lung volume (DLCO < LLN, VA < LLN, KCO < ULN); (E) Localized loss of lung volume or incomplete lung expansion (DLCO < LLN, VA < LLN, KCO > ULN); (F) Pulmonary vascular abnormality (DLCO < LLN, VA normal); and (G) Alveolar hemorrhage, polycythemia, increased blood flow (left to right shunt, or post-exercise).

Results: Initially, 61% of patients exhibited at least one of seven pulmonary function abnormalities (LLN = –1.645), which decreased to 22% by 390 days post-recovery. Considering day-to-day variability, 68% of patients showed improvement by the final visit, while 30% had unchanged lung function from the initial assessment. Computed tomography (CT) scans revealed ground-glass opacities in 33% of patients. One year after infection, DLCO z-scores accounted for 29% of the variation in HRCT fibrosis scores. No significant correlation was found between recovery length and lung function improvement based on z-scores.

Conclusion: Twenty-Two percent of patients who recovered from severe COVID-19 continued to show at least one lung function abnormality one year after recovery, indicating a prolonged impact of COVID-19 on lung health.

The data is in SPSS (.sav) format. Once the file is open you will find the labels for each parameter under the "VARIABLE VIEW" tab.