Differential equations are an increasingly essential tool used in public health epidemiology, enabling the modeling of communicable disease spread in a population and informing the development, implementation, and assessment of public health interventions. As public health professionals increasingly rely on GenAI for data management and data analysis, understanding the mathematical equations underlying a communicable disease model becomes crucial. This essay provides an accessible overview of ordinary first-order differential equations, including the linear first-order differential equation (i.e., exponential growth model) and the nonlinear first-order differential equation (i.e., SIR model), for the public health epidemiologist. We explain each type of differential equation and show a step-by-step solution to facilitate comprehension. A real-world application of the use of first-order differential equations in public health is, briefly, presented. Then, we discuss how real-world data, first-order differential equations, data analysis and predictive data modeling can contribute to the development of insights that guide the development, deployment, and assessment of public health interventions by public health decision-makers for a population. In the appendix, sample prompts are provided to help leverage the power of GenAI in the use of mathematical models in public health epidemiology. This technical review aims to provide the epidemiologist with an understanding of first-order differential equations function in the context of communicable disease modeling at a community- or population-level for public health decision-making and action.