scholarly journals Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

2017 ◽  
Vol 4 (3) ◽  
pp. 160721 ◽  
Author(s):  
A. A. Hill ◽  
M. Crotta ◽  
B. Wall ◽  
L. Good ◽  
S. J. O'Brien ◽  
...  
Keyword(s):  
Big Data ◽  
Food Safety ◽  
Food Chain ◽  
Surveillance System ◽  
Genetic Data ◽  
Data Sources ◽  
Processing Temperature ◽  
High Fidelity ◽  
Safety Surveillance ◽  
Foodborne Infection

Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining ‘big’ data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

scholarly journals Food safety surveillance through a risk based control programme: Approach employed by the Belgian Federal Agency for the safety of the food chain

2006 ◽  
Vol 28 (4) ◽  
pp. 140-154 ◽  
Author(s):  
J.‐P. Maudoux ◽  
C. Saegerman ◽  
C. Rettigner ◽  
G. Houins ◽  
X. Van Huffel ◽  
...  
Keyword(s):  
Food Safety ◽  
Food Chain ◽  
Federal Agency ◽  
Control Programme ◽  
Safety Surveillance

scholarly journals FOOD SAFETY IN THE SUPPLY CHAIN USING BLOCKCHAIN TECHNOLOGY

2019 ◽  
Vol 18 (4) ◽  
pp. 41-48
Author(s):  
Sławomir Jarka
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Food Safety ◽  
Food Chain ◽  
Food Production ◽  
Added Value ◽  
Food Supply Chain ◽  
Chain Management ◽  
Blockchain Technology ◽  
Food Production System

The purpose of the article is to determine the importance of blockchain technology in food supply chain management. A practical reference to the adopted research aim was to indicate the usefulness of blockchain technology to build trust between food chain stakeholders. Research shows that the properties of blockchain technology can enable it to solve many problems and shortcomings of the current food production system. Its added value is primarily a significant increase in transparency of operations among all stakeholders using big data in all parts of the food chain.

scholarly journals A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature

2019 ◽  
Vol 20 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Zenhwa Ouyang ◽  
Jan Sargeant ◽  
Alison Thomas ◽  
Kate Wycherley ◽  
Rebecca Ma ◽  
...  
Keyword(s):  
Big Data ◽  
Veterinary Medicine ◽  
Scoping Review ◽  
Full Text ◽  
Medical Literature ◽  
Animal Health ◽  
Genetic Data ◽  
Health Data ◽  
Data Sources ◽  
Steady Increase

AbstractResearch in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

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.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

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