Digit Health
Global Vaccine Epidemiology and Modelling Department (VEM), Sanofi Pasteur, Singapore, Singapore.
Published: March 2021
Background: Asia has been at the forefront of leveraging big data and digital technologies to strengthen measures against SARS-CoV-2 spread. Understanding strengths and challenges of these new approaches is important to inform improvements and implementation. In this review, we aimed to explore how these tools were utilized in four countries in Asia to facilitate COVID-19 preventative control measures.
Methods: We conducted a pragmatic review of English-language literature and web-based information in Pubmed, MedRxiv, national and international public health institution websites and media sources between 1st January-3rd August 2020 to identify examples of big data and digital technologies to facilitate COVID-19 preventative control measures in Taiwan, South Korea, Hong Kong, and Singapore. Results were summarized narratively by common technological themes, and examples of integration highlighted.
Results: Digital tools implemented included real-time epidemiological dashboards, interactive maps of case location, mobile apps for tracing patients' contacts and geofencing to monitor quarantine compliance. Examples of integration of tools included linkage of national health and immigration databases to identify high-risk individuals in Taiwan, and the use of multiple digital surveillance sources to map patients' movements in South Korea. Challenges in balancing privacy and public good were identified.
Conclusions: Digital technologies have facilitated and strengthened traditional public health measures for prevention of SARS-CoV-2 spread in Asia. Resolving issues around privacy concerns would improve future preparedness, implementation speed and uptake of digital measures. The significant technological advances and lessons learned can be adopted or adapted by other countries to ensure public health preparedness for future waves of COVID-19 and other pandemics.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995298 | PMC |
http://dx.doi.org/10.1177/20552076211002953 | DOI Listing |
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFMater Horiz
January 2025
Institute of Biomass Engineering, Key Laboratory of Energy Plants Resource and Utilization, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou, 510642, China.
Conversion of nitrogen (N) to ammonia (NH) is a significant process that occurs in environment and in the field of chemistry, but the traditional NH synthesis method requires high energy and pollutes the environment. In this work, the charge, orbital and spin order of the single-atom Fe loaded on heteroatom (X) doped-MoCS (X = B, N, O, F, P and Se) and its synergistic effect on electrochemical nitrogen reduction reaction (eNRR) were investigated using well-defined density functional theory (DFT) calculations. Results revealed that the X-element modified the charge loss capability of Fe atoms and thereby introduced a net spin through heteroatom doping, resulting in the magnetic moment modulation of Fe.
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFFront Artif Intell
January 2025
RV University, Bengaluru, India.
Introduction: Cyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning.
View Article and Find Full Text PDFAm J Neurodegener Dis
December 2024
School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran.
Unlabelled: This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.
Objectives: To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.
Methods: A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.
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