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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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Healthcare Associated Infections II: Preventive Measures01:22

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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
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Related Experiment Video

Updated: Aug 2, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Preemptive Epidemic Information Transmission Model Using Nonreplication Edge Node Connectivity in Health Care

Chandu Thota1, Constandinos X Mavromoustakis1, George Mastorakis2

  • 1Department of Computer Science, University of Nicosia, Nicosia Cyprus.

Big Data
|April 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Preemptive Information Transmission Model (PITM) to enhance medical data delivery during epidemics. PITM optimizes transmission speed and reliability, ensuring seamless access to sensitive health information.

Keywords:
conditional reliabilityepidemic broadcastmedical data transmissionpruning treereplication-less connection

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Area of Science:

  • Medical Informatics
  • Network Engineering
  • Epidemiology

Background:

  • Advancements in information and communication technologies have improved medical data organization and transmission.
  • The increasing volume of digital communication necessitates optimized accessibility and transmission of sensitive medical data.
  • Ensuring reliable medical data transmission is crucial, especially in epidemic regions.

Purpose of the Study:

  • To introduce the Preemptive Information Transmission Model (PITM) for prompt medical data delivery.
  • To design a transmission model that minimizes communication overhead in epidemic regions.
  • To improve the availability and reduce delays in transmitting sensitive medical data.

Main Methods:

  • The Preemptive Information Transmission Model (PITM) utilizes a noncyclic connection procedure and preemptive forwarding.
  • Replication-less connection maximization is achieved through pruning tree classifiers based on communication time and delivery balancing.
  • Conditional selection of infrastructure units ensures reliable data forwarding.

Main Results:

  • The proposed PITM model reduces connection replications, enhancing edge node availability.
  • The model optimizes transmissions, communication time, and minimizes delays in medical data delivery.
  • Seamless information availability is achieved even within epidemic regions.

Conclusions:

  • The Preemptive Information Transmission Model (PITM) effectively improves the promptness and reliability of medical data transmission.
  • PITM ensures better data delivery, reduced communication time, and fewer delays, particularly in critical situations like epidemics.
  • The model's design supports efficient and seamless access to vital medical information.