Data in brief
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The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. ⋯ This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting.
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The data article refers to the paper titles "Impact of malnutrition on long-term survival in adult patients after elective cardiac surgery" [1]. The data refer to the analysis of the relationship between baseline malnutrition and long-term mortality after cardiac surgery. Baseline demographic, nutritional, and medical history data were collected for each enrolled patient. ⋯ ROC analysis was performed to analyze prognostic value of baseline and perioperative variables on long-term mortality. Univariate and multivariate logistic regression analysis of predictors of 3- and 8-year mortality were performed. Kaplan-Meyer curves, describing the impact of baseline and perioperative characteristics on 3- and 8-year survival were also performed.
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The COVID-19 pandemic has created a global health emergency and has a huge impact on the health care workers, especially on their mental health. The dataset presented was an assessment of COVID-19 related knowledge, attitude, practices and its effects on the mental health of frontline healthcare workers in Pakistan. The data were collected using a snowball sampling technique. ⋯ The dataset includes 476 healthcare workers in Pakistan. The dataset will help to prevent and curb the spread of COVID-19 among health workers and contribute to policymakers. Furthermore, our dataset provides detailed insights into different risk factors of psychological problems, and it may be served as the reference for various in-depth surveys.
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Thirty-six anesthesia departments in 36 hospitals in four provinces of China where an outbreak of COVID-19 occurred were surveyed. We found that there were ten anesthesiologists (5 male and 5 female) who contracted the infection after performing intubation, as well as 4 nurses (1 male and 3 female) who contracted the infection after assisting with the intubation. This is a retrospective investigation and no intervention was applied. ⋯ Four nurses who assisted with intubations contracted COVID-19. One of these nurses was in critical condition but was eventually discharged with a loss of 50 days of clinical service. The remaining three nurses have had mild symptoms so far, but one is still hospitalized.
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The article shows the possibilities of Data Set "Interactive Statistics and Intelligent Analytics of the Balanced State of the Regional Economy of Russia in Terms of Big Data and Blockchain - 2020". For creation of the data set, we formed time rows of the values of the selected indicators, which characterize the balance of Russia's regional economy. The indicators are systematized and classified into two categories. ⋯ The data set's data are presented in the form of an interactive map of Russia's regional economy. Map's color shows categories that are assigned to regions and the borders of regions, as well as information on each region's position in the 2020 rating. The data set allows for large-scale studies of Russia's regional economy with application of technologies of Big Data processing, machine learning, and intellectual analytics.