MATHEMATICAL MODELS FOR PREDICTING THE EPIDEMIC TRENDS OF BRUCELLOSIS

Abstract

Objectives: This review aims to provide a comprehensive analysis of mathematical models in epidemiology, specifically focusing on Brucellosis. The objectives include examining the classification, characteristics, and practical applications of various mathematical models, distinguishing between data-driven and mechanism models, and exploring the implications for enhancing public health efforts in addressing Brucellosis outbreaks. Methods: A thorough literature review was conducted by searching PubMed and the China National Knowledge Infrastructure for relevant scientific articles. The selected studies were analyzed to identify key aspects of mathematical models. The focus was on understanding how these models contribute to predicting and managing the spread of infectious diseases, with a special emphasis on Brucellosis. Findings: The review identifies and categorizes mathematical models used in epidemiology, providing insights into their unique characteristics and practical applications. Distinctions between data-driven and mechanism models are explored based on various criteria, including their basis, interpretability, generalization, data requirements, and applicability. Novelty: This review contributes novelty by synthesizing information on mathematical models in epidemiology, offering a comprehensive overview with a specific focus on Brucellosis. The exploration of distinctions between data-driven and mechanism models, along with their implications for public health, adds a unique perspective to the existing literature.