scholarly journals Using Big Data Tools to Analyze Digital Footprint in the COVID-19 Pandemic: Some Public Health Ethics Considerations

2020 ◽  
pp. 101053952098436
Author(s):  
Olivia M. Y. Ngan ◽  
Adam M. Kelmenson
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Access ◽  
Public Health Ethics ◽  
Public Security ◽  
Personal Privacy ◽  
Viral Spread ◽  
Health Ethics

While many freedoms became halted by city lockdowns and restrictive travel bans amid coronavirus crisis, some countries and regions reopened with public health monitoring and surveillance measures in place. Technology applications such as real-time location data, geofencing technology, video camera footage, and credit card history are now used in novel and poorly understood ways to track movement patterns to stem viral spread. The use of big data analytics, which sometimes involve involuntary and unconsented data access and disclosure, raise public unease about data protection. The result is a balance between public health safety and ethical use of personal data that pushes the limits of privacy rights. Is it ethically permissible to use big data analytics instantiating the goal of public health by infringing on personal privacy in exchange for maximizing public security? Demonstrating the effectiveness of public health measures is difficult as scientific uncertainties and social complexities are presented. This article provides some public health ethics considerations in balancing benefits of public security and personal privacy infringement, supported with examples drawn from Asian countries and regions.

scholarly journals Big Data, Decision Models, and Public Health

2020 ◽  
Vol 17 (18) ◽  
pp. 6723
Author(s):  
Chien-Lung Chan ◽  
Chi-Chang Chang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Medical Decision ◽  
Mining Machine ◽  
Special Issue ◽  
Trade Offs

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

scholarly journals Privacy-aware Big Data Analytics as a service for public health policies in smart cities

2018 ◽  
Vol 39 ◽  
pp. 68-77 ◽  
Author(s):  
Marco Anisetti ◽  
Claudio Ardagna ◽  
Valerio Bellandi ◽  
Marco Cremonini ◽  
Fulvio Frati ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Health Policies ◽  
Public Health Policies

scholarly journals Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework

2020 ◽  
Vol 17 (17) ◽  
pp. 6161 ◽  
Author(s):  
Qiong Jia ◽  
Yue Guo ◽  
Guanlin Wang ◽  
Stuart J. Barnes
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Close Contact ◽  
Big Data Analytics ◽  
Contact Screening ◽  
Negative Impacts ◽  
Effective Use ◽  
And Control ◽  
Clear Information

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

scholarly journals Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Flavio Villanustre ◽  
Arjuna Chala ◽  
Roger Dev ◽  
Lili Xu ◽  
Jesse Shaw LexisNexis ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Causal Model ◽  
Clinical Manifestations ◽  
Big Data Analytics ◽  
Community Services ◽  
Reproductive Number ◽  
Severe Illness ◽  
Hpcc Systems

AbstractThis project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions.The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services.Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %.Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness.In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement.The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.

scholarly journals Emergence and Evolution of Big Data Analytics in HIV Research: Bibliometric Analysis of Federally Sponsored Studies 2000-2019

2021 ◽  
Author(s):  
Chen Liang ◽  
Shan Qiao ◽  
Bankole Olatosi ◽  
Tianchu Lyu ◽  
Xiaoming Li
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Federal Agencies ◽  
Research Topics ◽  
Aids Research ◽  
Hiv Aids

AbstractBackgroundThe rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data analytics. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data analytics in HIV/AIDS-related publications that were funded by the US federal agencies.MethodsWe identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies (i.e., NIH, ACF, AHRQ, CDC, HRSA, FDA, and VA) from 2000 to 2019 by integrating data from NIH ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.ResultsWe harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) University of California system, MD Anderson Cancer Center, and Harvard Medical School are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.ConclusionsWe identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and informed fast evolving research areas including HIV comorbidities, genomics, secondary analysis of EHR, social media, machine learning, and Deep Learning as future directions of HIV/AIDS research.

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