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∗-fuzzy measure model for COVID-19 disease
Abbas Ghaffari1, Reza Saadati2
1Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Advances in Difference Equations
|April 21, 2021
Summary
We present a novel ∗-fuzzy measure model to analyze COVID-19 dynamics. This mathematical framework explores fuzzy measure properties, including the Lebesgue-Radon-Nikodym theorem, for disease modeling.
Area of Science:
- Mathematical modeling
- Epidemiology
- Fuzzy mathematics
Background:
- COVID-19 presents complex dynamics requiring advanced modeling techniques.
- Traditional epidemiological models may not fully capture the uncertainties inherent in disease spread.
Purpose of the Study:
- To introduce a novel mathematical framework, the ∗-fuzzy measure model, for analyzing COVID-19.
- To explore fundamental properties of ∗-fuzzy measures within the context of disease modeling.
Main Methods:
- Development of the ∗-fuzzy measure model tailored for COVID-19.
- Investigation of key properties of ∗-fuzzy measures.
- Application of the Lebesgue-Radon-Nikodym theorem to the fuzzy measure framework.
Main Results:
- The ∗-fuzzy measure model provides a new approach to understanding COVID-19 transmission.
- Properties of ∗-fuzzy measures, including the Lebesgue-Radon-Nikodym theorem, are shown to be relevant for disease analysis.
Conclusions:
- The ∗-fuzzy measure model offers a promising tool for epidemiological research on COVID-19.
- Further research can extend this model to incorporate more complex disease dynamics and uncertainties.
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