In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak -- the patient zero or index patient -- requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks

Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean

Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

This article discusses Big Data's use as a surveillance tool for the spread of Corona Virus Disease 2019 (COVID-19), both in Indonesia and the world. In Indonesia, the range of COVID-19 is increasingly sporadic, causing mass panic and Indonesia's geographical characteristics, which will be difficult when this spread could not control quickly. Researchers are conducting several studies to overcome this pandemic, including supervision, features, handling, mobility, patient interaction, treatment evaluation, and the biological structure. These studies become data and lead to Big Data. This article explores how to use Big Data analysis to monitor the spread of COVID-19 as a communication process that reflects mediated communication as a form of mobility and spatial relationships in communication practices. The method used in this article is a literature review and uses meta-synthesis techniques as its analysis. The literature sources used are articles in highly reputable international journals. Based on the reports, various ways to monitor the virus's spread, through public video data, GPS, and social media tracking, trace the patient's movement. Big Data can also provide data collaboration for viruses and pathogens for further research as digital mediated communication is anchored by the diversity of places and the mobility of people, data, and objects

Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs

Visual analytics, a technique aiding data analysis and decision making, is a novel tool that allows for a better understanding of the context of complex systems. Public health professionals can greatly benefit from this technique since context is integral in disease monitoring and biosurveillance. We propose a graphical tool that can reveal the distribution of an outcome by time and age simultaneously.We introduce and demonstrate multi-panel (MP) graphs applied in four different settings: U.S. national influenza-associated and salmonellosis-associated hospitalizations among the older adult population (≥65 years old), 1991-2004; confirmed salmonellosis cases reported to the Massachusetts Department of Public Health for the general population, 2004-2005; and asthma-associated hospital visits for children aged 0-18 at Milwaukee Children's Hospital of Wisconsin, 1997-2006. We illustrate trends and anomalies that otherwise would be obscured by traditional visualization techniques such as case pyramids and time-series plots.MP graphs can weave together two vital dynamics--temporality and demographics--that play important roles in the distribution and spread of diseases, making these graphs a powerful tool for public health and disease biosurveillance efforts

An Introduction to Current Trends in Meat Microbiology and Hygiene

<jats:title>Abstract</jats:title><jats:sec> <jats:title>Purpose of Review</jats:title> <jats:p>This editorial review aims to provide readers with an introduction to the <jats:italic>Current Clinical Microbiology Report</jats:italic> Special Issue “Meat Microbiology and Hygiene.” It will provide an overview of overarching trends and developments in this field, introduce the articles presented in this Special Issue, and attempt to offer a glimpse into the future of meat microbiology and hygiene.</jats:p> </jats:sec><jats:sec> <jats:title>Recent Findings</jats:title> <jats:p>Meat production has been subjected to transformative changes within the last decade, and the focus of assuring meat safety has shifted to account for changing consumer demands as well as new microbial risks such as strains carrying antimicrobial resistance determinants.</jats:p> </jats:sec><jats:sec> <jats:title>Summary</jats:title> <jats:p>Assuring that meat products meet high safety standards remains crucial to consumers worldwide. New risk-based meat safety assurance systems leveraging latest technological advances are needed to protect consumers and promote public health.</jats:p> </jats:sec&gt

Big Data Analytics in Immunology: A Knowledge-Based Approach

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow