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Introduction: There are aspects of COVID-19 pathogenesis that are still unknown. Objective: To determine the relationship between severity, mortality and viral replication in patients with COVID-19. Methods: Clinical characteristics, severity and mortality of 203 patients hospitalized for COVID-19 were analyzed and correlated with viral load (VL) and threshold cycle (TC) at admission;nasopharyngeal swab was obtained. Results: Mean VLs in surviving patients with mild to moderate, moderate to severe and severe disease were the following: 6.8 x 10(6), 7.6 x 10(7) and 1.0 x 10(9), respectively;and in patients with critical disease who died, VL was 1.70 x 10(9). TCs were 26.06, 24.07, 22.66 and 21.78 for the same groups. In those who died, a higher mean VL was observed at admission in comparison with those who survived (1.7 x 10(9) vs 9.84 x 10(6);p < 0.001). A significant correlation was observed between VL, severity and death (r = 0.254, p < 0.045 and r = 0.21, p < 0.015). High VL was associated with increased in-hospital mortality in comparison with low VL (OR = 2.926, p < 0.017). Conclusion: SARS-CoV-2 VL determined at hospital admission might classify risk simultaneously with other factors described in COVID-19.
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The COVID-19 pandemic made big cracks in the tourism industry, being responsible for the almost imminent decrease in tourism activities around the world simultaneously, reducing international visits by 75%, causing a loss of world GDP of 2.4 trillion dollars. The tourism industry has always been supported by technology, being the information systems and different mobile applications, its main allies in the promotion and dissemination of information and tourism activities worldwide. The following article seeks to highlight the importance of information systems as a strategy for the recovery of tourism after the COVID-19 pandemic.
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Purpose/Objectives: To describe the rapid implementation of remote patient portal activation in a pediatric integrated delivery network during the initial phase of the COVID-19 pandemic. We sought to increase the percentage of patients with active portal status upon discharge by 15% (absolute change) across inpatient units within 3 months. Design/Methods: A multidisciplinary taskforce utilized QI tools (fishbone diagram, process map) to identify barriers to successful inpatient patient portal activation. PDSA cycles included rapid cycle training of the remote MyChart enrollment workflow for hospital unit clerks (HUCs), increased patient education about portal functionality, standardized portal enrollment workflow across all inpatient units, and improved visibility of portal status for clinical staff and HUCs. The primary measure was the percentage of admitted patients with active MyChart status upon discharge. Baseline data was collected retrospectively via Epic Reporting Workbench. A patient portal activation dashboard was used to monitor progress on a weekly basis. We used statistical process control charts to examine the impact. Results: We completed training and clinical decision support tools (Unit Manager view and MyChart® status Epic column) creation within 2 weeks from the start of the initiative. Active online patient portal status upon hospital discharge increased from 44% to 66% (a 22% absolute increase) within 12 weeks. The process demonstrated a consecutive upward trend of 5 or more data points, consistent with special cause variation at the end of April 2020. During the same time period, the percentage of online patient portal activation within 7 days after hospital discharge increased from 6.0% to 24%, and the patient portal offer rate increased from 73% to 85% across all inpatient units. Conclusion/Discussion: We rapidly and effectively implemented a remote proxy portal activation process in the inpatient setting. Remote activation, training, clinical decision support, multidisciplinary involvement, and emphasis on the portal as integral to ongoing care allowed us to dramatically increase activation.
ABSTRACT
The COVID-19 pandemic made big cracks in the tourism industry, being responsible for the almost imminent decrease in tourism activities around the world simultaneously, reducing international visits by 75%, causing a loss of world GDP of 2.4 trillion dollars. The tourism industry has always been supported by technology, being the information systems and different mobile applications, its main allies in the promotion and dissemination of information and tourism activities worldwide. The following article seeks to highlight the importance of information systems as a strategy for the recovery of tourism after the COVID-19 pandemic. © 2022 IEEE Computer Society. All rights reserved.
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The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact on areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. To go beyond current epidemic prediction possibilities, this article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures.
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The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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The accelerated propagation of Sars-Cov2 and the consequent Covid-19 disease in the world has led the public administrations of different countries to use the advantages provided by disruptive technologies to effectively and efficiently manage the large volume of data generated. This communication presents the main characteristics and advances that distinguish the development of the initiative called Epidempredict for Covid19 in Panama. The project involves the implementation of a cloud platform that facilitates data analysis in a distributed, collaborative and secure form. This platform will enable efficient data ingestion, administration, analysis, visualization and export. The development will be a solution oriented to VUCA (Volatile, Uncertain, Complex and Ambiguous) environments. The implementation integrates tools and resources supported by technologies such as Artificial Intelligence (AI) and Machine Learning (ML) integrating models, predictive algorithms and dashboards that enable decision making based on real and meaningful data. The project proposes the use of a neuro-hybrid model, based on a SIR model with artificial intelligence, combining several algorithms. This initiative aims to support the health authorities of the Panamanian public administration by facilitating decision making and the adoption and implementation of precise strategic actions in the field of health and public welfare. © 2021 AISTI.
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The surprise arrival of the COVID-19 pandemic produced an accelerated transition in all educational institutions, forcing them to take advantage of digital technologies and the Internet to ensure that their operation could keep going. In this document, a study of various scientific articles, reports, publications, and existing documentation on the digital transformation processes launched in the different Latin American universities was carried out, presenting the methodological proposals promoted toward the new modalities of remote education, the reinvention of administrative processes, and the support provided to the university community to reduce the digital divide. An online survey was designed to know the advances in the digital transformation (DT) of 20 universities in Latin America. Outcomes of the online survey supply insights in four key DT objectives: teaching and learning, student support, research, and administration. Also, a case study of the implementation and monitoring of the DT model at the Technological University of Panama and its projections was considered. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.