Interrupted Time Series (ITS) analysis is a cornerstone technique for evaluating the population-level impact of public health interventions, particularly in observational settings where randomized trials are infeasible. This chapter introduces ITS models as applied to infectious disease surveillance data, highlighting both classical and Bayesian approaches for modeling temporal trends and quantifying intervention effects. We begin by outlining the assumptions and structure of traditional ITS models, then extend to autoregressive models and ARIMA formulations that account for autocorrelation and non-stationarity. This chapter also explores Bayesian alternatives, including Bayesian Structural Time Series (BSTS), which allow for flexible modeling of uncertainty and prior information. Emphasis is placed on real-world implementation, model diagnostics, and the interpretation of estimated effects using credible intervals and forecasting. Through the integration of simulated datasets and real-world epidemiological applications, this chapter illustrates how ITS models provide rigorous evidence to support public health decision-making and guide timely responses to emerging infectious disease patterns.