Prepare the data | Points | Area of interest | Friction surface | Input data | Calculate travel time | Plot results

Prepare the data | Points | Area of interest | Friction surface | Input data | Calculate travel time | Plot results

Accessing age and population information | Brief example of using accessor functions | An example use from the package | Using these as S3 methods in an R package | Conclusion

World data | Australian Bureau of Statistics (ABS) data | Accessing Functions | abs_age_lga() | abs_age_state() | ABS data | Education by state data for 2006 -2020 | Education by state data for 2020 | Employment by LGA for 2016 | Number of people in each household by LGA for 2016 | LGA age population for 2016 for all states and LGAs | LGA age population for 2020 for all states and LGAs | State age population for 2020 | Epidemiology / disease modelling data | Transmission probabilities from Eyre

Create a new synthetic matrix from all POLYMOD data | Generating a Next Generation Matrix | Applying Vaccination Rates | Fitting a new model with asymmetric terms

Quick example using Australian data | Quick example using world data | What next? | A More in depth example | Predicting the contact rate | Plotting | Note | Applying the model across all settings. | Fit to all settings | Predict to all settings

Introduction: What is an SIR model? | An SIR Model with homogenous mixing | Comparison to other age matrices | Calculating reproductive number - R0

Creating a next generation matrix (NGM) | Applying vaccination to an NGM

extrapolate polymod | For interest's sake: visualising the empirical contact rate data