scholarly journals Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258649
Author(s):  
Leander Melms ◽  
Evelyn Falk ◽  
Bernhard Schieffer ◽  
Andreas Jerrentrup ◽  
Uwe Wagner ◽  
...  
Keyword(s):  
Risk Factors ◽  
Exploratory Data Analysis ◽  
Regional Distribution ◽  
Information Aggregation ◽  
Data Driven ◽  
Previous Exposure ◽  
Front Line ◽  
Federal States ◽  
Exploratory Data ◽  
Epidemiological Parameters

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

scholarly journals Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic

2021 ◽  
Author(s):  
Leander Melms ◽  
Evelyn Falk ◽  
Bernhard Schieffer ◽  
Andreas Jerrentrup ◽  
Uwe Wagner ◽  
...  
Keyword(s):  
Risk Factors ◽  
Exploratory Data Analysis ◽  
Regional Distribution ◽  
Information Aggregation ◽  
Data Driven ◽  
Previous Exposure ◽  
Front Line ◽  
Federal States ◽  
Exploratory Data ◽  
Epidemiological Parameters

AbstractPandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

scholarly journals Exploratory Data Analysis in a Six-Year Longitudinal Study in Healthy Brain Aging

2019 ◽  
Author(s):  
Jaime Gómez-Ramírez ◽  
Marina Ávila Villanueva ◽  
Belén Frades Payo ◽  
Teodoro del Ser Quijano ◽  
Meritxell Valentí Soler ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Cognitive Decline ◽  
Exploratory Data Analysis ◽  
Brain Aging ◽  
Wide Range ◽  
Exploratory Data ◽  
Longitudinal Dataset

AbstractAlzheimer’s Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in early stages of the disease is a paramount obstacle to formulate a preclinical and predictive model of AD. Not surprisingly, the AD drug approval rate is one of the lowest in the industry, an exiguous 0.4%. The identification of risk factors, preferably obtained by the subject herself, is sorely needed given that the incidence of Alzheimer’s disease grows exponentially with age [Ferri et al., 2005], [Ganguli and Rodriguez, 2011].During the last 7 years, researchers at Proyecto Vallecas have collected information about the project’s volunteers, aged 70 or more. The Proyecto Vallecas dataset includes information about a wide range of factors including magnetic resonance imaging, genetic, demographic, socioeconomic, cognitive performance, subjective memory complaints, neuropsychiatric disorders, cardiovascular, sleep, diet, physical exercise and self assessed quality of life. The subjects in each visit were diagnosed as healthy, mild cognitive impairment (MCI) or dementia.In this study we perform Exploratory Data Analysis to summarize the main characteristics of this unique longitudinal dataset. The objective is to characterize the evolution of the collected features over time and most importantly, how their dynamics are related to cognitive decline. We show that the longitudinal dataset of Proyecto Vallecas, if conveniently exploited, holds promise to identifying either factors promoting healthy aging and risk factors related to cognitive decline.

scholarly journals Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics

2021 ◽  
Author(s):  
Ratanond Koonchanok ◽  
Parul Baser ◽  
Abhinav Sikharam ◽  
Nirmal Kumar Raveendranath ◽  
Khairi Reda
Keyword(s):  
Visual Analytics ◽  
Exploratory Data Analysis ◽  
Visual Analysis ◽  
Data Driven ◽  
Belief Elicitation ◽  
Mixed Analysis ◽  
Limited Support ◽  
Interactive Visualizations ◽  
Exploratory Data ◽  
Confirmatory Analysis

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.

scholarly journals Exploiting the Potential of VGI Metadata to Develop A Data-Driven Framework for Predicting User’s Proficiency in OpenStreetMap Context

2019 ◽  
Vol 8 (11) ◽  
pp. 492
Author(s):  
Gangothri Rajaram ◽  
KR Manjula
Keyword(s):  
Exploratory Data Analysis ◽  
Principal Component ◽  
Data Driven ◽  
User Classification ◽  
Analysis Phase ◽  
User Clustering ◽  
Data History ◽  
Exploratory Data ◽  
Two Phases ◽  
Geospatial Databases

Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues on OpenStreetMap (OSM), a particular case of VGI in recent times. This research examines the hypothesis of evaluating the proficiency of the contributor to assess the credibility of the data contributed. The proposed framework consists of two phases, namely, an exploratory data analysis phase and a learning phase. The former explores OSM data history to perform feature selection, resulting in “OSM Metadata” summarized using principal component analysis. The latter combines unsupervised and supervised learning through K-means for user-clustering and multi-class logistic regression for user classification. We identified five major classes representing user-proficiency levels based on contribution behavior in this study. We tested the framework with India OSM data history, where 17% of users are key contributors, and 27% are unexperienced local users. The results for classifying new users are satisfactory with 95.5% accuracy. Our conclusions recognize the potential of OSM metadata to illustrate the user’s contribution behavior without the knowledge of the user’s profile information.

scholarly journals Exploratory Data Analysis in Schools: A Logic Model to Guide Implementation

2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Matthew B. Courtney
Keyword(s):  
Data Analysis ◽  
School Improvement ◽  
Exploratory Data Analysis ◽  
Logic Model ◽  
Data Driven ◽  
School Activities ◽  
Use Of Data ◽  
Continuous School Improvement ◽  
Data Analysis Procedure ◽  
Exploratory Data

Exploratory data analysis (EDA) is an iterative, open-ended data analysis procedure that allows practitioners to examine data without pre-conceived notions to advise improvement processes and make informed decisions. Education is a data-rich field that is primed for a transition into a deeper, more purposeful use of data. This article introduces the concept of EDA as a necessary structure to be embedded in school activities by situating it within the literature related to data-driven decision making, continuous school improvement systems, and action research methodologies. It also provides a succinct six-part framework to guide practitioners in establishing EDA procedures.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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