What are the Challenges in Time Series Analysis for Epidemiology?
Analyzing time series data in epidemiology poses several challenges: 1. Missing Data: Incomplete data can hinder the analysis and lead to biased results. 2. Non-Stationarity: Many epidemiological time series are non-stationary, meaning their statistical properties change over time. 3. Complex Interactions: Multiple factors, such as environmental and socio-economic conditions, can interact in complex ways, affecting disease patterns. 4. Data Quality: Inaccurate or inconsistent data collection methods can affect the reliability of the analysis. 5. Lag Effects: Delays between exposure to risk factors and the onset of disease can complicate the analysis.
