Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A...

Area of Science:

• Epidemiology
• Computational Biology
• Data Science

Background:

• The COVID-19 pandemic significantly impacted global daily life, necessitating research into disease spread dynamics.
• Numerous studies have focused on developing predictive models for the spread of COVID-19.

Purpose of the Study:

• To review open-access datasets used in COVID-19 regression modeling.
• To present current literature on Artificial Intelligence (AI) methods for COVID-19 spread prediction.
• To discuss the application of Machine Learning (ML) and Evolutionary Computing (EC) in regressing COVID-19 epidemiology curves.

Main Methods:

• An electronic literature search was conducted across multiple databases.
• The review focused on AI-based approaches for modeling COVID-19 epidemiological spread.
• Analysis included datasets and literature related to regression tasks in disease modeling.

Main Results:

• AI-based algorithms demonstrate significant applicability in modeling COVID-19 spread.
• Machine Learning and Evolutionary Computing methods are effective for regressing epidemiological curves.
• Various open-access datasets are foundational for current COVID-19 regression modeling research.

Conclusions:

• AI algorithms are valuable tools for understanding and predicting COVID-19 epidemiological spread.
• These computational approaches can be crucial in managing current and future pandemics.
• Further research leveraging AI can enhance pandemic preparedness and response strategies.