The possible dynamical states in a compartmented model of disease.
compartmented model of disease
A disease model that represents the progression of a disease as a set of discrete compartments with transitions possible between them. Transitions typically occur with some base probability, which might be fixed or might vary across the course of the simulation. See Hethcote for a survey,
continuous time
A simulation mode in which events occur at unique times represented by real numbers. No two events ever happen simultaneously, but they can be separated by an arbitrarily small interval. Continuous-time simulations can be made statistically exact and run faster for situations in which there are long periods where no events occur.
discrete time
A simulation mode in which time progresses in single integer timesteps. During each timestep a collection of events can occur. Discrete-time simulations can be easier to code and understand.
dynamical state
The state of a node or edge at some point in the simulation. These typically reflect the compartments of the simulation, but may be more complex and comprise a vector of information.
A simulation event that changes the state of the underlying network or simulation. Events can occur in continuous time or discrete time.
event function
A function called when an event fires to perform the action required. Event functions take four arguments: the Dynamics object running the simulation, the current simulation time, the network, and the element at which the event occurs (which will be selected by the chosen process dynamics). Elements are typically either nodes or edges, depending in the locus at which the event occurs.
A “place” at which dynamics can occur, that is to say, where nodes can change compartments and any other tasks can happen. Each event is associated with a particular locus: the locus contains the set of nodes or edges to which the event may be applied, while the event defines chat happens.
posted event
An event posted for a definite future time. The process dynamics will execute the posted events at the appropriate time
process dynamics
The simulation approach used, which selects how and when each event fires. Process dynamics execute events in time order from two possible sources: a random distribution that chooses an event based on their relative probability or rate; and any posted event that has been scheduled.
stochastic process
A process whose exact progression is determined by random variables drawn from particular probability distributions.
stochastic dynamics
Also known as Gillespie dynamics, this process dynamics operates in continuous time with one event occurring at each time point.
synchronous dynamics
A process dynamics using discrete time, where a simulation passes through a sequence of discrete timesteps which may include several (or no) events happening.