👉 Physicians often use mathematical models to understand and predict the spread of diseases, assess treatment efficacy, and manage healthcare resources efficiently. For instance, they might employ differential equations to model how infections change over time within a population, capturing the dynamics of susceptible, infected, and recovered individuals (SIR models). These models help in estimating key parameters like the basic reproduction number (R0), which indicates how many people, on average, one infected person will infect in a fully susceptible population. Additionally, logistic growth models can describe the progression of diseases through different stages, while statistical methods and machine learning algorithms are increasingly used to analyze large datasets, identify patterns, and make accurate predictions about patient outcomes and disease trends. This mathematical approach enables doctors to make informed decisions, tailor treatments, and allocate resources more effectively during public health crises.
