Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

Intergenerational Transfers and Public Expenditure on the Elderly in Modern Britain

1988 ◽  
Vol 8 (2) ◽  
pp. 129-146 ◽  
Author(s):  
Paul Johnson ◽  
Jane Falkingham
Keyword(s):  
Social Services ◽  
Elderly Population ◽  
Public Expenditure ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Generational Conflict ◽  
The Us ◽  
Families With Children ◽  
Poor Children

ABSTRACTIn the United States, much attention has recently been directed to the issue of whether the welfare system has become over-generous to the retired population, at the expense of families with children. The proportion of the US elderly population living in poverty has fallen significantly in the last fifteen years while the number of poor children has increased rapidly, and it has been suggested that this lack of investment in the next generation of workers may have disastrous longterm consequences for the U.S. economy. This paper considers whether similar trends are evident in Britain. It reviews data on the poverty and income of the elderly population, and finds little unequivocal evidence of relative economic gain over the last two decades, although it is clear that many children have suffered from the recent rise in unemployment-induced poverty. It also looks at direct public expenditure on the elderly through both the pension and the health and personal social services systems, and finds no evidence of a transfer of public resources away from children and towards the elderly population. The paper concludes that the British welfare state has been remarkably neutral in its allocation of resources between generations, and that, in the British context, any discussion of inter-generational conflict for welfare resources establishes a false dichotomy, because economic inequality within broad age groups is much greater than inequality between age groups.

scholarly journals The Influence of Covid-19 Vaccine on Daily Cases, Hospitalization, and Death Rate in Tennessee: A Case Study in the United States

2021 ◽  
Author(s):  
Ali Roghani
Keyword(s):  
Death Rate ◽  
Age Groups ◽  
Vaccination Strategy ◽  
The Elderly ◽  
The United States ◽  
Hospitalization Rate ◽  
Older Age ◽  
Study Cohort ◽  
Lower Death Rate

The COVID-19 outbreak highlights the vulnerability to novel infections, and vaccination remains a foreseeable method to return to normal life. However, infrastructure is inadequate for the whole population to be vaccinated immediately. Therefore, policies have adopted a strategy to vaccinate the elderly and vulnerable population while delaying others. This study uses the Tennessee official statistic from the onset of COVID vaccination (17th of December 2021) to understand how age-specific vaccination strategies reduce daily cases, hospitalization, and death rate. The result shows that vaccination strategy can significantly influence the numbers of patients with COVID-19 in all age groups and lower hospitalization and death rates just in older age groups. The Elderly had a 95% lower death rate from December to March; however, and no change in the death rate in other age groups. The Hospitalization rate was reduced by 80% in this study cohort for people aged 80 or older, while people who were between 50 to 70 had almost the same hospitalization rate. The study indicates that vaccination targeting older age groups is the optimal way to avoid higher transmissions and reduce hospitalization and death rate for older groups.

scholarly journals Racial disparities in COVID-19 mortality are driven by unequal infection risks.

2020 ◽  
Author(s):  
Jon Zelner ◽  
Rob Trangucci ◽  
Ramya Naraharisetti ◽  
Alex Cao ◽  
Ryan Malosh ◽  
...  
Keyword(s):  
Racial Disparities ◽  
Data Science ◽  
Age Groups ◽  
Case Fatality ◽  
The United States ◽  
Mortality Rates ◽  
Bayesian Regression ◽  
Mortality Data ◽  
Incidence And Mortality ◽  
The U.S

Background. As of August 5, 2020, there were more than 4.8M confirmed and probable cases and 159K deaths attributable to SARS-CoV-2 in the United States, with these numbers undoubtedly reflecting a significant underestimate of the true toll. Geographic, racial-ethnic, age and socioeconomic disparities in exposure and mortality are key features of the first and second wave of the U.S. COVID-19 epidemic. Methods. We used individual-level COVID-19 incidence and mortality data from the U.S. state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. Findings. In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than Whites for all groups other than Native Americans. Of these, Blacks experienced the greatest burden of confirmed and probable COVID-19 infection (Age- standardized incidence = 1,644/100,000 population) and mortality (age-standardized mortality rate 251/100,000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.6 (95% CI = 5.5, 5.7) and 6.9 (6.5, 7.3) times higher than Whites, respectively. We also found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. Interpretation. This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as the U.S. state of Michigan, are driven primarily by variation in household, community and workplace exposure rather than case-fatality rates. Funding. This work was supported by a COVID-PODS grant from the Michigan Institute for Data Science (MIDAS) at the University of Michigan. The funding source had no role in the preparation of this manuscript.

scholarly journals STUDI LITERATUR: PENGARUH TERAPI PIJAT REFLEKSI KAKI TERHADAP KUALITAS TIDUR LANJUT USIA

2021 ◽  
Vol 7 (1) ◽  
pp. 8-13
Author(s):  
Chaerun Nissa ◽  
Ashar Prima ◽  
Fauziah Hamid Wada ◽  
Puji Astuti ◽  
Salamah T Batubara
Keyword(s):  
United States ◽  
Literature Review ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Age Groups ◽  
The Elderly ◽  
Scientific Paper ◽  
The United States ◽  
Google Scholar ◽  
World Health

WHO states that Indonesia's population is the fourth largest population after China, India, and the United States. According to the 2013 World Health Statistics data, the population of China is 1.35 billion, India is 1.24 billion, the United States 313 million, and Indonesia is in fourth place with 242 million WHO population predicts that by 2020 the estimated number of Indonesia's elderly will be around 80,000,000. Cases of insomnia in the elderly are higher than in other age groups, which is 12–39%. One therapy that can overcome sleep disorders in the elderly is foot reflexology massage therapy. This literature review aims to determine the effect of foot reflexology massage in the elderly who experience sleep disorders. The design in this scientific paper is a literature review search using an electronic data base that is google scholar and pubmed. The keywords used in the search are elderly, foot reflexology, sleep of quality. The inclusion criteria used in the article are full text accessible in English and Indonesian, the year of the journal used is limited to the last ten years. The results found 1 article from Google Scholar and 2 articles from PubMed discussing the effectiveness of foot reflexology massage on improving sleep quality in the elderly. Literature review results from the three articles show that foot reflexology is effective in improving sleep quality in the elderly.  

scholarly journals Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 752
Author(s):  
Anna Kovacs-Györi ◽  
Alina Ristea ◽  
Clemens Havas ◽  
Michael Mehaffy ◽  
Hartwig H. Hochmair ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Temporal Analysis ◽  
Data Sources ◽  
Urban Systems ◽  
Urban Dynamics ◽  
Analysis Methods ◽  
Data Driven Approach ◽  
Spatio Temporal

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

scholarly journals Utazás előtti tanácsadás és betegedukáció a magyar utazóknál

2018 ◽  
Vol 159 (9) ◽  
pp. 357-362
Author(s):  
Ingrid Lengyel ◽  
Péter Felkai
Keyword(s):  
Travel Medicine ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
International Travel ◽  
The European Union ◽  
Elderly Age ◽  
Similar Survey ◽  
The Given

Abstract: Introduction: According to international surveys, over half of the travellers face some kind of health issue when travelling. The overwhelming majority of travel-related illnesses can be prevented with pre-travel medical consultations, but the syllabus and content of the consultation have to match the travel habits and culture of the given society. Aim: This publication explores the specificities and travel habits of Hungarian travellers. Method: One hundred participants of a travel exhibition completed a survey about their international travel. As the survey was not representative, the data could only be processed through simple statistical methods. However, since the exhibition was presumably attended by those wishing to travel, the conclusions drawn from the results are worth publishing, since no similar survey in Hungary has been published before. Results: Based on the suitable classification of age groups in travel medicine, 11% of the participants were adolescents / young adults (aged 15–24), 81% adults (25–59) and 8% elderly (60–74). Twenty-eight percent of the participants travel multiple times a year, 40% yearly and 32% of them less frequently; 16% of the adults, 8% of the adolescents and 4% of the elderly age group travel multiple times a year. Conclusions: The travel destinations of Hungarian travellers have remained practically unchanged since a study was conducted 13 years ago: the vast majority (95%) travelled within Europe, 2% to the United States, and 11% of them elsewhere. Since Hungarians do not travel to endemic areas, only 5% consulted their general practitioners (GPs) prior to travelling, and 29% did when they had to be vaccinated. Forty-two percent of those wishing to travel never consult their GPs, even though 29% of them are aware of some chronic illness. Instead, 51% gather their health information from the internet and only 6% from their doctors. By the contradiction between the poor health status of the majority of Hungarian travellers and the negligence of seeking pre-travel advice, our survey clearly points out the importance of the propagation of doctor’s advice before trips, even if the travellers visit exclusively non-endemic countries like the European Union. Orv Hetil. 2018; 159(9): 357–362.

scholarly journals COVID-19 Vaccination and the Daily Cases, Hospitalizations and Death Rates: a Case Study of Tennessee in the United States. (Preprint)

2021 ◽  
Author(s):  
Ali Roghani
Keyword(s):  
Death Rate ◽  
Age Groups ◽  
Vaccination Strategy ◽  
The Elderly ◽  
Health Department ◽  
The United States ◽  
Census Bureau ◽  
Hospitalization Rate ◽  
Death Rates ◽  
Vaccination Rates

BACKGROUND The COVID-19 outbreak highlights the vulnerability to novel infections, and vaccination remains a foreseeable method to return to normal life. However, infrastructure is inadequate for the vaccination of the whole population immediately. Therefore, policies have adopted a strategy to vaccinate the elderly and vulnerable populations while delaying others. OBJECTIVE This study uses the Tennessee official statistic to understand how age-specific vaccination strategies reduce daily cases, hospitalization, and death rate. METHODS The research used publicly available data of COVID-19, including vaccination rates, positive cases, hospitalizations, and death from the health department of Tennessee. This study targeted from the first date of vaccinations, December 17, 2020, to March 3, 2021. The rates were adjusted by data from U.S. Census Bureau (2019), and the age groups were stratified at ten-year intervals from the age of 21. RESULTS The result shows that vaccination strategy can reduce the numbers of patients with COVID-19 in all age groups with lower hospitalization and death rates in older. The elderly had a 95% lower death rate from December to March, while no change in the death rate in other age groups. The hospitalization rate was reduced by 80% for people aged 80 or older, while people who were between 50 to 70 had almost the same hospitalization rate. CONCLUSIONS The study indicates that targeting older age groups for vaccination is the optimal way to avoid higher transmissions, reduce hospitalization and death rates. CLINICALTRIAL

scholarly journals Behavioural response to heterogeneous severity of COVID-19 explains temporal variation of cases among different age groups

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Benjamin Steinegger ◽  
Lluís Arola-Fernández ◽  
Clara Granell ◽  
Jesús Gómez-Gardeñes ◽  
Alex Arenas
Keyword(s):  
Temporal Variation ◽  
Disease Surveillance ◽  
Data Science ◽  
Age Groups ◽  
Mechanistic Explanation ◽  
The Elderly ◽  
Seasonal Effects ◽  
Additional Stress ◽  
Severity Of The Disease ◽  
Low Prevalence

Together with seasonal effects inducing outdoor or indoor activities, the gradual easing of prophylaxis caused second and third waves of SARS-CoV-2 to emerge in various countries. Interestingly, data indicate that the proportion of infections belonging to the elderly is particularly small during periods of low prevalence and continuously increases as case numbers increase. This effect leads to additional stress on the health care system during periods of high prevalence. Furthermore, infections peak with a slight delay of about a week among the elderly compared to the younger age groups. Here, we provide a mechanistic explanation for this phenomenology attributable to a heterogeneous prophylaxis induced by the age-specific severity of the disease. We model the dynamical adoption of prophylaxis through a two-strategy game and couple it with an SIR spreading model. Our results also indicate that the mixing of contacts among the age groups strongly determines the delay between their peaks in prevalence and the temporal variation in the distribution of cases. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Optimizing the impact of low-efficacy influenza vaccines

2018 ◽  
Vol 115 (20) ◽  
pp. 5151-5156 ◽  
Author(s):  
Pratha Sah ◽  
Jan Medlock ◽  
Meagan C. Fitzpatrick ◽  
Burton H. Singer ◽  
Alison P. Galvani
Keyword(s):  
Age Groups ◽  
Population Level ◽  
The Elderly ◽  
The United States ◽  
Vaccine Uptake ◽  
Influenza Vaccines ◽  
Health Practitioner ◽  
Specific Distribution ◽  
Life Years ◽  
The Impact

The efficacy of influenza vaccines varies from one year to the next, with efficacy during the 2017–2018 season anticipated to be lower than usual. However, the impact of low-efficacy vaccines at the population level and their optimal age-specific distribution have yet to be ascertained. Applying an optimization algorithm to a mathematical model of influenza transmission and vaccination in the United States, we determined the optimal age-specific uptake of low-efficacy vaccine that would minimize incidence, hospitalization, mortality, and disability-adjusted life-years (DALYs), respectively. We found that even relatively low-efficacy influenza vaccines can be highly impactful, particularly when vaccine uptake is optimally distributed across age groups. As vaccine efficacy declines, the optimal distribution of vaccine uptake shifts toward the elderly to minimize mortality and DALYs. Health practitioner encouragement and concerted recruitment efforts are required to achieve optimal coverage among target age groups, thereby minimizing influenza morbidity and mortality for the population overall.

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