Spatial models work by incorporating spatially referenced data, which includes the locations of disease cases and possible environmental factors. These models use various statistical techniques to account for spatial autocorrelation, which is the tendency for locations close to each other to have similar characteristics.
1. Data Collection: Collect spatially referenced data on disease cases and potential risk factors. 2. Model Selection: Choose an appropriate spatial model based on the nature of the data and the research question. 3. Parameter Estimation: Use statistical methods to estimate the parameters of the model. 4. Model Validation: Validate the model using techniques like cross-validation or comparison with independent data. 5. Interpretation: Interpret the results to understand the spatial patterns and identify risk factors.
