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This summary is machine-generated.

Artificial Intelligence (AI) models analyze zoonotic diseases, which spread from animals to humans. This study reviews machine learning and deep learning for predicting pathogen spread and identifying risk factors.

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Area of Science:

  • Veterinary Medicine
  • Epidemiology
  • Infectious Diseases

Background:

  • Zoonotic diseases (zoonoses) are infections naturally transmitted between animals and humans.
  • Over 70% of emerging infectious diseases originate from animals.
  • Understanding disease transmission is crucial for public health.

Purpose of the Study:

  • To synthesize and analyze Artificial Intelligence (AI) approaches for studying zoonotic diseases.
  • To understand predictive models for identifying risk factors and developing mitigation strategies.
  • To review machine learning and deep learning applications in zoonotic disease research.

Main Methods:

  • Literature survey of machine learning and deep learning applications.
  • Analysis of AI models used for zoonotic pathogen research.
  • Synthesis of findings on predictive modeling for zoonotic diseases.

Main Results:

  • Machine learning and deep learning are frequently employed for predicting zoonotic pathogens.
  • These AI models are also used to identify factors contributing to pathogen presence.
  • The study highlights the utility of AI in understanding disease dynamics.

Conclusions:

  • AI, particularly machine learning and deep learning, offers powerful tools for zoonotic disease research.
  • Predictive modeling aids in identifying risks and informing mitigation strategies.
  • Further research can leverage AI for enhanced zoonotic disease surveillance and control.