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Related Concept Videos

Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

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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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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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Models of Health Promotion and Illness Prevention I01:25

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A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Principles of Disease Surveillance01:26

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review

Bader M Albahlal1

  • 1College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|October 14, 2023
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Summary

Hybrid models integrating emerging technologies like AI and IoT offer a robust strategy for preventing and monitoring infectious diseases, enhancing global public health security.

Keywords:
AI algorithmsIoT devicesbig data analyticsblockchaindeep learning

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Health Informatics

Background:

  • Emerging infectious diseases pose significant global health threats, necessitating advanced prevention and monitoring strategies.
  • The COVID-19 pandemic highlighted the need for innovative approaches to manage and control disease outbreaks.
  • Traditional methods require augmentation with modern technologies for enhanced effectiveness.

Purpose of the Study:

  • To review hybrid models for infectious disease prevention and monitoring, focusing on those developed post-COVID-19.
  • To explore the integration of emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), big data, and blockchain.
  • To propose a conceptual framework for a hybrid model incorporating these technologies.

Main Methods:

  • Comprehensive literature review of hybrid models for infectious disease management.
  • Analysis of studies integrating AI, IoT, big data, and blockchain in disease surveillance.
  • Development of a conceptual framework based on reviewed models and technologies.

Main Results:

  • Hybrid models integrating emerging technologies demonstrate potential for secure contact tracing and source isolation.
  • Blockchain ensures data security, IoT enables real-time monitoring, and big data analytics facilitate predictive insights.
  • AI enhances diagnostic capabilities and outbreak prediction.

Conclusions:

  • Hybrid models incorporating emerging technologies offer a comprehensive approach to infectious disease prevention and monitoring.
  • The proposed framework provides a blueprint for developing advanced, technology-driven public health surveillance systems.
  • Continued research and development are crucial for optimizing these models to combat infectious diseases globally.