Several methods are employed for analyzing time series data in epidemiology: 1. Descriptive Analysis: Initial examination of data using graphical methods like line plots to visualize trends and patterns. 2. Decomposition: Breaking down the time series data into its components (trend, seasonality, and irregularities). 3. Smoothing Techniques: Methods like moving averages and exponential smoothing to reduce noise and highlight underlying patterns. 4. Autoregressive Integrated Moving Average (ARIMA): A popular model that combines autoregression, differencing, and moving averages to analyze and forecast time series data. 5. Seasonal Decomposition of Time Series (STL): A technique used to separate seasonal components from the trend and irregular components. 6. Fourier Analysis: Used to identify and quantify periodic components in the data.
