Introduction | Getting to know the basics of the package | Using surveillance in selected contexts | Implementing surveillance in routine monitoring | Discussion

Model class | Data structure | Modeling and inference | Simulation

Model class | Data structure | Modeling and inference | Simulation

Model class | Data structure | Modeling and inference | Simulation

Introduction | Surveillance Data | Surveillance Algorithms | Discussion and Future Work | Acknowledgements

Introduction | Surveillance data | Model formulation | Function call and control settings | Conclusion

Introduction | Preliminaries | LR and GLR-charts | Control-settings | Discussion

Polygon representations | Cubature methods | Illustrations | 1. Product Gauss cubature: polyCub.SV() | 2. Two-dimensional midpoint rule: polyCub.midpoint() | 3. Adaptive cubature for isotropic functions: polyCub.iso() | 4. Integration of the bivariate Gaussian density: polyCub.exact.Gauss() | Benchmark

Data | Berlin norovirus counts | Age-structured contact matrix | Modelling | One-week-ahead forecasts

Modelling | One-week-ahead forecasts | Long-term forecasts | Example with the first test period | For all test periods

Modelling | One-week-ahead forecasts | Long-term forecasts

Modelling | One-week-ahead forecasts | Long-term forecasts

Modelling | One-week-ahead forecasts | Long-term forecasts

One-week-ahead forecasts | Long-term forecasts