Introduction | Epidemiological modeling | Technical description of the simulation framework | Model construction and data analysis: Basic examples | Extending SimInf: New models | Benchmark | Conclusion | Acknowledgments | Pseudo-code for the default simulation solver | Illustration of scheduled events | C code for the SIR model

Overview | The Basic Syntax: Transitions | A Simple SI Model | Adding Recovery: The SIR Model | Defining Variables and Population Size | Defining the Total Population | Data Types: Integer vs. Double | Creating and Running the Model | Handling Edge Cases: Division by Zero | Creating the Model | Incorporating Global and Local Data | A Two-Farm Model | Defining Global and Local Data | Initial Conditions for Multiple Nodes | Modeling Open Populations: The Empty Set | Example: Predator-Prey Dynamics | Non-Exponential Passage Times (Erlang Distribution)

Overview | A first example: External transfer events | Varying probability of picking individuals | Enter events: Adding individuals | Example: Births entering a population | Weighted sampling for enter events | Exit events: Removing individuals | Example: Mortality | Weighted sampling for exit events | Internal transfer events: Moving within a node | Example: Vaccination of cattle herd | Stochastic events using proportion | Example: Proportional culling | Processing order of simultaneous events | Example: Multiple events at the same time | Summary

Extract trajectory data with trajectory() | Calculate prevalence from a trajectory using prevalence() | Visualize a trajectory with plot() | Aggregated View (Median and Range) | Individual Node View | Prevalence Plots | Summary