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This study reviews how mathematical models have been developed to represent and predict disease spread using differential equations and network frameworks. Compartmental models such as SIS, SIR, SEIR, and SEIATR describe temporal changes in susceptible, infected, and recovered populations, whereas network\u2010based models\u2014including contact, trade, and spatial networks\u2014capture real\u2010world heterogeneity and transmission structure. By analyzing previous modeling studies on diseases such as COVID\u201019, avian influenza, and African swine fever, this paper demonstrates how mathematical modeling aids in forecasting outbreaks, optimizing control interventions, and guiding public health policies. 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