The Pandemic Shut-In: Reactivating Wells Shut-In in Spring/Summer 2020 – The Impact on 10-Year Forecast and EUR Outlook: A Duvernay Oil Case Study

The COVID-19 pandemic forced Canadian oil and gas operators to cut crude oil production by almost 1 MMb/d in the first half of 2020 due to low oil prices driven by reduced demand. This study explores the forecast and EUR performance of unconventional horizontal oil wells producing from the Duvernay Formation in central Alberta that were shut-in versus those that continued to produce uninterrupted throughout the reduced production period. How were forecasted production and EURs impacted? Did the manner in which the wells were completed play a role? This paper investigates these questions and more in a regional case study of 95 unconventional Duvernay oil wells using public data and a fully automated, physio-statistical, predictive analytical production forecasting tool. The bases of the performance comparison were the results of a 10-year forecast and EUR outlook for the wells evaluated in January, 2020 before the production slow down, and then re-evaluated in January, 2021, 12 months later, after the wells that were shut-in were back on production. In general, wells that continued producing uninterrupted throughout the study period exhibited significantly improved forecast and EUR performance over wells that were shut-in. Analyzing the performance of the largest field (Cygnet with 32 wells), with respect to lateral length, the results pointed to shorter wells that were shut-in exhibiting the poorest performance, where the wells' EUR performance degraded by 7% on average. The proppant intensity study for the same wells told a similar story, with shut-in wells with smaller fracs exhibiting negligible EUR improvement (0.4%) compared to the other categories of wells, with respect to frac size and shut-in status. A proximity study investigated two pads, one with only shut-in wells and the other with only non-shut-in wells, with the results pointing to competitive drainage between individual wells despite the overall performance of a given pad being neutral. Copyright 2022, Unconventional Resources Technology Conference (URTeC)

Telehealth Utilization among Low Income Population during COVID-19: An Analysis of COVID-19 Research Database

The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate the influence of demographic, socio-economic, and behavioral factors on telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services. © 2023 IEEE Computer Society. All rights reserved.

Accounting for partisanship and politicization: Employing Benford's Law to examine misreporting of COVID-19 infection cases and deaths in the United States

The unprecedented contagion of the SARS-CoV-2 virus, causative of COVID-19, has spawned watershed economic, social, ethical, and political upheaval—catalyzing severe polarization among the global populace. Ostensibly, to demonstrate the most appropriate path towards responding to the virus outbreak, public officials in the United States ("U.S.”), representing both Democratic and Republican parties, stand accused of unduly influencing COVID-19 records in their respective jurisdictions. This study investigates the role political partisanship may have played in decreasing the accuracy of publicly reported COVID-19 data in the U.S. Leveraging social identity theory, we contend that public officials may have manipulated the reporting records in accounting for COVID-19 infection cases and deaths to validate the effectiveness of political party objectives. We employ Benford's Law to assess misreporting and evaluate the integrity of county-level COVID-19 reporting data through the construction of four distinct political party classifications. Specifically, we cross the county voting majority for the 2016 presidential candidate for each U.S. state (Democratic and Republican) with the 2020 gubernatorial political party (Democratic and Republican) in which each county resides. For the sample period of January 21, 2020 through November 3, 2020 (Election Day), the study's results suggest that the reported COVID-19 infection cases and deaths in the U.S. violate Benford's Law in a manner consistent with underreporting. Our analysis reveals that Democratic counties demonstrate the smallest departures from Benford's Law while Republican counties demonstrate the greatest departures. © 2023 Elsevier Ltd

An AI-Based Platform Architecture with Situational Awareness for Travel Plans

Demand for non-face-to-face services is increasing in all industries due to the impact of the global pandemic caused by COVID-19 over the past three years. This phenomenon also occurs in the travel industry without exception. Travel consumption patterns are changing in the form of FIT (Foreign Independent Tour) rather than package tours provided by existing travel agencies. Now, we are entering the era of travel tech using smartphones. To this end, technological efforts are being made to simultaneously address the two characteristics of individual free travel and non-face-to-face situations. In this paper, we propose an AI-based platform architecture with situation awareness for travel plans. It provides a personalized service using the ontology-based mobile service so that tourists can make travel plans according to their own circumstances, after considering the situation of individual tourists through sensor collection data while analyzing the congestion situation of tourist destinations through public data. The mobile platform consists of a total of four layers (Infrastructure, Cross-Cutting Infrastructure, Domain, Application), and has been implemented in an environment of Android 8.0 or higher and iOS 11.0 or higher through the AWS website. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Predictive Model for R0(SARS-COV2)

Due to the rapid spread of the COVID-19, scientists are constantly monitoring the evolution of the number of infections in a region. In particular, the basic reproductive number (R0) is studied, because it indicates if the number of cases will increase and the infection will last, or if it will decrease and stability will be reached. The present contribution is focused on forecasting this ratio, based on the extreme gradient boosting tree approach. Gradient reinforcement trees are used. Using public data of the COVID-19 outbreak in the Caribbean and some countries, this value is computed. © 2022 IEEE.

Open Government Data as a panacea against corruption and Mismanagement: An analysis of Open Public Data on COVID-19-related procurement at the sub-national level

The COVID-19 pandemic has had a huge impact on public purchasing. As a response to the necessity to react quickly and procure goods that were urgently needed, governments set new rules allowing for quicker procurement processes. Those processes often seemed opaque and incomprehensible, creating a space for corruption and inefficiency. Publishing information as open data helps shine a light on procurement data. This paper aims to assess how transparency policies and open data strategies can allow civil society to monitor government expenditures, particularly expenditures made in moments of crisis. The authors looked deeper into procurement data at the regional level, exemplified by the German state of North Rhine-Westphalia (NRW). In an analysis which includes the compliance with international standards such as the Open Contracting Standard and the level of completeness of the data published, the authors found an incomplete data landscape with little information available. Major concerns for the data quality are poorly written documentation, lack of data standardization and law enforcement, and missing information on key variables. © 2022 Owner/Author.

CovidLens: Visually Understanding the Covid-19 Indicators through the Lens of Mobility Data

Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens. © 2022 IEEE.

“We Are Trying to Communicate the Best We Can”: Understanding Districts’ Communication on Twitter During the COVID-19 Pandemic

While educators’ uses of social media for purposes such as professional learning and networking are now well-established, our understanding of how educational institutions use social media—including to engage key stakeholders during periods of crisis—is limited. In this study, we used a public data mining research approach to examine how K–12 school districts in the United States used Twitter as a communication tool during a critical period of the COVID-19 pandemic, March-April, 2020. Through a three-step grounded theory approach of 1,357 district tweets from 492 school districts, we found that districts worked to build community and share time-sensitive announcements in alignment with social media crisis communication recommendations. Announcements were more common during the early stages of the pandemic (and were engaged more collaboratively), with community-building posts more common later on. This study demonstrates how researchers can use publicly available (social media) data to understand districts’ communication priorities and strategies during and beyond periods of crisis. © The Author(s) 2022.

Blending data to understand the economic impact of COVID-19.

The data demands during the pandemic heightened the need to blend information from numerous sources to get a more timely and granular picture of economic developments. Ongoing efforts include the Chicago Fed's weekly retail sales estimate, the Census Bureau's work on higher-frequency state-level retail sales data, the Federal Reserve Board's computations of business closures and weekly payrolls, and the academic Opportunity Insights team's estimates of spending, business revenues and employment by income and ZIP code.

Model, Integrate, Search.. Repeat: A Sound Approach to Building Integrated Repositories of Genomic Data

A wealth of public data repositories is available to drive genomics and clinical research. However, there is no agreement among the various data formats and models;in the common practice, data sources are accessed one by one, learning their specific descriptions with tedious efforts. In this context, the integration of genomic data and of their describing metadata becomes—at the same time—an important, difficult, and well-recognized challenge. In this chapter, after overviewing the most important human genomic data players, we propose a conceptual model of metadata and an extended architecture for integrating datasets, retrieved from a variety of data sources, based upon a structured transformation process;we then describe a user-friendly search system providing access to the resulting consolidated repository, enriched by a multi-ontology knowledge base. Inspired by our work on genomic data integration, during the COVID-19 pandemic outbreak we successfully re-applied the previously proposed model-build-search paradigm, building on the analogies among the human and viral genomics domains. The availability of conceptual models, related databases, and search systems for both humans and viruses will provide important opportunities for research, especially if virus data will be connected to its host, provider of genomic and phenotype information. © 2022, The Author(s).

Machine Learning-Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study.

BACKGROUND: The onset and development of the COVID-19 pandemic have placed pressure on hospital resources and staff worldwide. The integration of more streamlined predictive modeling in prognosis and triage-related decision-making can partly ease this pressure. OBJECTIVE: The objective of this study is to assess the performance impact of dimensionality reduction on COVID-19 mortality prediction models, demonstrating the high impact of a limited number of features to limit the need for complex variable gathering before reaching meaningful risk labelling in clinical settings. METHODS: Standard machine learning classifiers were employed to predict an outcome of either death or recovery using 25 patient-level variables, spanning symptoms, comorbidities, and demographic information, from a geographically diverse sample representing 17 countries. The effects of feature reduction on the data were tested by running classifiers on a high-quality data set of 212 patients with populated entries for all 25 available features. The full data set was compared to two reduced variations with 7 features and 1 feature, respectively, extracted using univariate mutual information and chi-square testing. Classifier performance on each data set was then assessed on the basis of accuracy, sensitivity, specificity, and received operating characteristic-derived area under the curve metrics to quantify benefit or loss from reduction. RESULTS: The performance of the classifiers on the 212-patient sample resulted in strong mortality detection, with the highest performing model achieving specificity of 90.7% (95% CI 89.1%-92.3%) and sensitivity of 92.0% (95% CI 91.0%-92.9%). Dimensionality reduction provided strong benefits for performance. The baseline accuracy of a random forest classifier increased from 89.2% (95% CI 88.0%-90.4%) to 92.5% (95% CI 91.9%-93.0%) when training on 7 chi-square-extracted features and to 90.8% (95% CI 89.8%-91.7%) when training on 7 mutual information-extracted features. Reduction impact on a separate logistic classifier was mixed; however, when present, losses were marginal compared to the extent of feature reduction, altogether showing that reduction either improves performance or can reduce the variable-sourcing burden at hospital admission with little performance loss. Extreme feature reduction to a single most salient feature, often age, demonstrated large standalone explanatory power, with the best-performing model achieving an accuracy of 81.6% (95% CI 81.1%-82.1%); this demonstrates the relatively marginal improvement that additional variables bring to the tested models. CONCLUSIONS: Predictive statistical models have promising performance in early prediction of death among patients with COVID-19. Strong dimensionality reduction was shown to further improve baseline performance on selected classifiers and only marginally reduce it in others, highlighting the importance of feature reduction in future model construction and the feasibility of deprioritizing large, hard-to-source, and nonessential feature sets in real world settings.

The conspiracy of Covid-19 and 5G: Spatial analysis fallacies in the age of data democratization.

In a context of mistrust in public health institutions and practices, anti-COVID/vaccination protests and the storming of Congress have illustrated that conspiracy theories are real and immanent threat to health and wellbeing, democracy, and public understanding of science. One manifestation of this is the suggested correlation of COVID-19 with 5G mobile technology. Throughout 2020, this alleged correlation was promoted and distributed widely on social media, often in the form of maps overlaying the distribution of COVID-19 cases with the instillation of 5G towers. These conspiracy theories are not fringe phenomena, and they form part of a growing repertoire for conspiracist activist groups with capacities for organised violence. In this paper, we outline how spatial data have been co-opted, and spatial correlations asserted by conspiracy theorists. We consider the basis of their claims of causal association with reference to three key areas of geographical explanation: (1) how social properties are constituted and how they exert complex causal forces, (2) the pitfalls of correlation with spatial and ecological data, and (3) the challenges of specifying and interpreting causal effects with spatial data. For each, we consider the unique theoretical and technical challenges involved in specifying meaningful correlation, and how their discarding facilitates conspiracist attribution. In doing so, we offer a basis both to interrogate conspiracists' uses and interpretation of data from elementary principles and offer some cautionary notes on the potential for their future misuse in an age of data democratization. Finally, this paper contributes to work on the basis of conspiracy theories in general, by asserting how - absent an appreciation of these key methodological principles - spatial health data may be especially prone to co-option by conspiracist groups.

Epidemiological Analysis of Coronavirus Disease 2019 (COVID-19) in 2 Cities in China Based on Public Data.

Based on the public data from the health departments of Tianjin and Shenzhen, we conducted a comparative analysis of the coronavirus disease 2019 (COVID-19) epidemic situation between these 2 cities. The aim of this study was to evaluate the role of public data in epidemic prevention and control of COVID-19, providing a scientific advice for the subsequent mitigation and containment of COVID-19 prevalence.