Intermittent data migration | Batch data migration

Introduction | Seasonal data | Determining season | Determining the disease specific threshold | Applying the main algorithm | Seasonal onset and burden levels | Seasonal offset and multiple waves | Summary of seasonal onset and burden levels | Plot the comprehensive seasonal analysis | Investigate historical estimates | Example with incidence

Methodology | Applying the multiple waves algorithm | Estimate disease-specific threshold | Estimate multiple waves | Plot the comprehensive seasonal analysis with multiple waves

The diseasy data model | A bitemporal data model | valid_from and valid_until | from_ts and until_ts | Automatic data-coupling | Features | Observables | Stratifications | Naming convention | Creating FeatureHandlers | Computing features | Retrieving features | Aggregators | Putting it all together | Creating a diseasystore | ds_map | Key join filter | Testing your diseasystore | Exposing the period of data availability | Limiting support for some database backends

Introduction | Feature identification | Feature implementation | The birth FeatureHandler | The sex FeatureHandler | The age FeatureHandler | The n_positive FeatureHandler | The n_hospital FeatureHandler | The n_admission FeatureHandler | The final product | Configuring the diseasystore | Retrieving features | Combining features | No stratification | Stratifying by a custom age group | Stratifying by generation

Methodology | Peak observations | Weighting | Distribution and optimisation | Burden levels | Applying the seasonal_burden_levels() algorithm | Use the intensity_levels method | Use the peak_levels method | Compare intensity_levels, peak_levels and mem algorithms | aedseo and mem levels

Methodology | Exponential growth rate | Applying the seasonal_onset algorithm | Visualising Growth Rates | Predicting Growth Rates | Summarising seasonal_onset results

Simulation | Plot seasonal waves | Example of positive trend (weekly observations) | Example of negative trend (monthly observations) | Example of no trend (daily observations) | Example of phase shift (daily observations) | Examples of different noise scenarios | Deterministic (no noise) | Poisson-distributed noise | Negative binomial-distributed noise (high overdispersion) | Examples of different epidemic concentrations | Pure sinusoidal season | Epidemic concentrated season

Available diseasystores | Using a diseasystore | Dynamically expanded | Time versioned | Automatic aggregation | Dropping computed features | diseasystore options

Type 1 and Type 2 history | A "timeline of timelines" | Summary | References

References

Introduction | Applying the algorithm | The aeddo package implements S3 methods | Formulation of the hiearchical generalzied linear model | Inference on individual groups | The rationale for employing the Gamma distribution as a second stage model | The three steps of the algorithm