OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided.

Guest editorial: Digital transformation strategy and impacts during emergency situations

In line with the interdisciplinarity of the Industrial Management and Data Systems (IMDS) journal, this Special Issue (SI) was focused on exploring the digital transformation phenomenon from different angles, with particular attention on the interplay between operations management, supply chain and information systems related fields, during emergencies and environmental uncertainty contexts. [...]this SI aims to inspire debate and discussion with scholars, practitioners and decision-makers working on governments by reporting the finest science and valuable practical and policy insights to advance the literature, practices and policy formulation. 2. [...]R&D's higher prior investment creates higher levels of digital technology, supporting the firm's resilience. [...]the authors point out that retailers can use blockchain for permission marketing strategies. The results reveal that top management support plays an important mediation effect. [...]the study points out the moderation effect that environmental factors exert on big data analytics adoption.

OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided. © 2022 IEEE.

FLIGHT TEST: A KEY MILESTONE FOR CLIMATENEUTRAL AIRCRAFT CONCEPTS VALIDATION AND CERTIFICATION

The global aviation industry has been massively impacted by the COVID crisis which lead to a collapse of the traffic and urged the need for a climate-neutral aviation. The sector is undergoing great mutations with the emergence of an urban air mobility based on eVTOL modules, electric and hydrogen airplanes for the short -haul transport and fuel efficient engines combined with sustainable aviation fuels for the long-haul flights. For years, Safran Data Systems's turn-key solutions for data collection, recording, transmission and processing have given aircraft manufacturers an edge on their flight test campaigns. Based on its expertise Safran Data Systems aims at assisting aircraft manufacturers in the validation and certification of tomorrow's concepts by expanding its portfolio on flight test instrumentation solutions for electric, hydrogen and emission efficient aircrafts. Proof-of-concepts are conducted to explore new innovative solutions such as HVDC electrical systems monitoring. © 2021 International Foundation for Telemetering. All rights reserved.

Analysis of Naturalistic Driving Events in Honolulu (Pre-COVID)

Dashboard cameras and sensors were installed in 233 taxi vans in Honolulu, Hawaii. They produced many hours of naturalistic driving data (NDD) between fall 2019 and spring 2020 in the form of 20 s recorded events. The study achieved its objectives to (1) collect data from naturalistic driving events where driving maneuvers caused acceleration or deceleration in any direction of 0.5g or higher, (2) develop a database suitable for statistical analysis, (3) derive basic statistics for all variables, and (4) investigate correlations between variables. The database included a total of 402 harsh events, of which were 398 near-crashes and 4 were crashes. Several variables such as road, environmental, driver, and vehicle characteristics were coded for each event. The installation of an NDD system by the taxi company proved to be a successful tool for coaching drivers, and for providing statistically significant insights into traffic safety factors relating to near-miss events, such as wider expressways come with a higher risk for near rear-end events;urban roads without parking lower the risk of near rear-end events;light traffic density significantly reduces the risk of rear-end events on freeways;and, mobile phone usage has a positive and significant coefficient that increases the risk of highway rear-end events. © ASCE.

An Overlooked Strategic Powerhouse: How Nurses Can Rise to the Challenge of Integrating Public Health and Primary Care

Although the United States is one of the wealthiest countries in the world and a leader in biomedical innovation, its health care system is consistently ranked among the worst in terms of cost and health outcomes. Americans have short life expectancies, high infant mortality and obesity rates, and soaring chronic disease rates compared with other wealthy nations. In 2021, the National Academy of Medicine (NAM) was charged with examining what it would take to improve US primary care. The NAM report described the practice of siloing public health from primary care or treating these areas as separate fields of scientific inquiry, practice, and billable service.1 NAM identified this separation as a key driver of poor health outcomes and health inequities in the United States. The Institute of Medicine (IOM) examined similar phenomena in a 2012 report, noting how the two fields tend to operate independently, despite complementary functions and common goals.2Where these silos persist, we see communication and process breakdowns at the point of care. For instance, when large swaths of Americans turned to trusted primary care providers for COVID-19 vaccine insights, their primary care providers did not always have the most up-todate information, in part because of a lack of interprofessional cohesion (including fragmented public health messaging and data systems). If we are to remedy such issues, a substantive paradigm shift must take place: We must move toward what DeSalvo et al.3 termed "Public Health 3.0." In this model, multiple sectors, specialties, and stakeholders form coalitions to mobilize data, people power, and resources in a strategic manner to advance health for all. To be truly strategic, we must think carefully about how to leverage nurses-who care for patients across the lifespan and in nearly all public health nursing (PHN) and primary care settings-within these coalitions.The 2021 NAM report urges health care teams to undertake the mission of integrating systems. However, NAM stops short of describing exactly how teams ought to accomplish this aim and the proposed makeup ofsaid teams. Like any group project, success will depend on the ability of teams to identify leaders and clearly delineate responsibilities. The purpose of this editorial is to explore the potential of PHN and primary care nurses and to describe the roles they might assume in the collaborative integration of their respective silos.

Issues on Japanese COVID-19 Exposure Notifications Application (COCOA)

This study investigates problems related to COCOA, which is a smartphone app officially provided by Japan's Ministry of Health, Labour and Welfare (MHLW) that is designed to notify users when they have been in close contact with coronavirus disease 2019 (COVID-19) positive persons, and thus help the government and healthcare organizations contain the spread of the virus. The information we have obtained thus far indicates that poor utilization rates of the app are due to significant program flaws, which caused the initial usage to be sluggish, as well as the failures of various health centers to adequately provide polymerase chain reaction (PCR) testing for COCOA notification recipients, which exacerbated sluggishness issues. Furthermore, a related survey revealed that although the government provides an integrated data system called the Health Center Real-time Information-sharing System on COVID-19 (Japanese abbreviation HER-SYS), information on fever outpatients (hospital names, locations, consultation times, presence or absence of PCR testing, etc.) corresponding to each local government is still not fully available. © 2022 IEEE.

Child vaccination rates fall to decade low amid pandemic

“Multiple disease outbreaks would be catastrophic for communities and health systems already battling COVID-19, making it more urgent than ever to invest in childhood vaccination and ensure every child is reached,” he said in a statement released on July 15. The pandemic has highlighted the importance of developing data systems that are timely and high quality in providing equitable access to immunisation for all, and allow real-time tracking of immunisation efforts, according to Jonathan Mosser, an assistant professor at the Institute for Health Metrics and Evaluation at the University of Washington’s School of Medicine, who was an author on The Lancet report. See PDF] Winluck Shayo, the chief executive officer of AfyaTrack, a health tech organization that tracks maternal and child health among Swahili-speaking communities in Africa, says the COVID-19 pandemic should serve as a lesson for governments across the world on how to sustain essential health services – such as childhood immunization – during the pandemic.

Designing a Sustainable Data Structure for Exploring Student Well-Being Patterns in Albemarle County Public Schools Throughout COVID-19

Student well-being has been affected by the COVID-19 pandemic. Albemarle County Public Schools (ACPS) has collected a significant and varied amount of K-12 student data throughout COVID-19. Researchers seek to utilize the student data to drive evidence-based policy changes with regard to ACPS student well-being. A structured data system for performing school-related research associated with the well-being of students throughout the pandemic does not exist. We have designed a sustainable, relational data structure for data consolidation and to advance the ongoing research initiatives related to COVID-19 student well-being in collaboration with ACPS. The data structure aims to play an important role in promoting student well-being policies through simplifying data collection, enhancing analysis, and acting as an ongoing tool that can support future phases of research. The design architecture includes a relational database populated with de-identified student data to be hosted in the cloud. Design implementation includes data cleaning, data preprocessing, populating the database, and querying data for validation. Specialized queries are utilized to answer the early questions posed to the data. Validation testing is performed to confirm the database is working as expected. Details of the data pipeline, validation, best data practices, and database design are discussed in the paper. © 2022 IEEE.

Being a Public Health Statistician During a Global Pandemic

In this perspective, I first share some key lessons learned from the experience of modeling the transmission dynamics of SARS-CoV-2 in India since the beginning of the COVID-19 pandemic in 2020. Second, I discuss some interesting open problems related to COVID-19 where statisticians have a lot to contribute to in the coming years. Finally, I emphasize the need for having integrated and resilient public health data systems: good data coupled with good models are at the heart of effective policymaking.

Development and formative evaluation of the Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS): applications for COVID-19 and beyond. / Élaboration et évaluation formative du Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) : applications durant la pandémie de COVID-19 et futures applications.

The COVID-19 pandemic highlighted limitations in the current public health data infrastructure, and the need for a comprehensive, real-time, centralized, user-friendly data management system suitable for both disease surveillance and outbreak management. To address these issues, the Canadian Forces Health Services Group developed the webbased Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS). This paper details the development of the CAF SOMS, provides formative evaluation results and includes a discussion of the lessons learned and intent to use the CAF SOMS in future to enhance the CAF's disease surveillance and outbreak management capability beyond COVID-19.

The Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS) was developed to address gaps in information management identified during the COVID-19 pandemic. Integrating a formative evaluation in the phased development and implementation helped to address issues with the system prior to its national roll-out. Lessons learned from its development, implementation and evaluation can inform further refinement and future applications of the CAF SOMS, and potentially of other public health information systems. Increased uptake of the system, integration with electronic records and alignment with the pan-Canadian Health Data Strategy may enhance responses to disease threats and improve CAF health outcomes.

Le Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) a été élaboré pour combler les lacunes en matière de gestion de l'information relevées pendant la pandémie de COVID-19. L'intégration d'une évaluation formative dans le processus d'élaboration et de mise en oeuvre par étapes a aidé à régler les problèmes liés au système avant son déploiement à l'échelle nationale. Les constats dégagés lors de l'élaboration, de la mise en oeuvre et de l'évaluation du système vont pouvoir contribuer à l'amélioration et aux applications futures du SSGE FAC et peut-être même d'autres systèmes d'information en santé publique. L'utilisation accrue du système, son intégration aux dossiers médicaux électroniques et son harmonisation avec la Stratégie pancanadienne de données sur la santé contribuerait vraisemblablement à lutter contre les menaces liées aux maladies et améliorer les résultats en matière de santé des FAC.

Intelligent System for Distributed Social Governance Based on Big Data

In recent years, promoting the synergy and intelligence of social governance, and improving the social governance system of co-construction, co-governance and sharing are important development directions for the country. As a production factor, data plays an increasingly critical role in social governance. How to realize the secure query, collaborative management, and intelligent analysis of multi-party massive data is the key issue to improve the effectiveness of social governance. In major public events such as the prevention and control of the COVID-19, distributed social governance faces low computing efficiency, poor multi-party credible coordination, and difficult decision-making for complex tasks. In response to the above challenges, this study proposes on big data based distributed social governance intelligent system based on secure multi-party computing, blockchain technology, and precise intelligence theory. The proposed system can support various applications of social governance that provide decision-making support for the improvement of social governance in the new era. © Copyright 2022, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

TemPredict: A Big Data Analytical Platform for Scalable Exploration and Monitoring of Personalized Multimodal Data for COVID-19

A key takeaway from the COVID-19 crisis is the need for scalable methods and systems for ingestion of big data related to the disease, such as models of the virus, health surveys, and social data, and the ability to integrate and analyze the ingested data rapidly. One specific example is the use of the Internet of Things and wearables (i.e., the Oura ring) to collect large-scale individualized data (e.g., temperature and heart rate) continuously and to create personalized baselines for detection of disease symptoms. Individualized data, when collected, has great potential to be linked with other datasets making it possible to combine individual and societal scale models for further understanding the disease. However, the volume and variability of such data require novel big data approaches to be developed as infrastructure for scalable use. This paper presents the data pipeline and big data infrastructure for the TemPredict project, which, to the best of our knowledge, is the largest public effort to gather continuous physiological data for time-series analysis. This effort unifies data ingestion with the development of a novel end-to-end cyberinfrastructure to enable the curation, cleaning, alignment, sketching, and passing of the data, in a secure manner, by the researchers making use of the ingested data for their COVID-19 detection algorithm development efforts. We present the challenges, the closed-loop data pipelines, and the secure infrastructure to support the development of time-sensitive algorithms for alerting individuals based on physiological predictors illness, enabling early intervention. © 2021 IEEE.

Implementing a digital system for contact tracing and case investigation during COVID-19 pandemic in San Francisco: a qualitative study.

During the COVID-19 pandemic, many health jurisdictions deployed digital informatics systems to support "manual" case investigation and contact tracing (CICT). This case study evaluates the implementation and use of a digital information system through the experiences of CICT workers in the City and County of San Francisco (CCSF). We conducted semi-structured, 90-min interviews with a sample of the CCSF CICT workforce (n = 37). Participants also completed standardized assessments of the digital system using the System Usability Scale (SUS). Qualitative analyses highlighted (1) the importance of digital tools to ensure rapid onboarding and effective data capture in a public health emergency; (2) the use of digital systems to support culturally sensitive care; and (3) the role of digitals tools in building supportive work environments. The mean SUS score was 70/100 (SD = 17), indicating relative ease of use. In summary, the analysis highlights the importance of digital tools to support manual CICT in the COVID-19 response.

Developmental impacts of the COVID-19 pandemic on young children: a conceptual model for research with integrated administrative data systems.

The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.