Import\Git\Project from git (using smart import)
Obviously dependencies need to be installed and since this is not a Maven or Ant project it must be checked what Eclipse installs.
Apache's implementation of RK4 is used: org.apache.commons.math3.ode.nonstiff.RungeKuttaIntegrator
To add Apache commons to an Eclipse project:
To use RungeKuttaIntegrator and other solvers from Apache Commons:
https://commons.apache.org/proper/commons-math/userguide/ode.html
"The user should describe his problem in his own classes which should implement the FirstOrderDifferentialEquations interface. Then they should pass it to the integrator they prefer among all the classes that implement the FirstOrderIntegrator interface."
So in the very first example SIR_ODE implements FirstOrderDifferentialEquations.
The SIR_ODE.getDimension method return the size of the array holding the cardinality of each compartment. So for this simple SIR example it is 3 because the compartments are S, I and R with as many differential equations for dS/dt dI/dt and dR/dt.
It is not mandatory to create a Maven project to use XChart
Check that you have correctly configured Git by following this procedure
Then, follow this step
Import\Git\Project from git (using smart import)
Both kinds of incidence are only equivalent if N is fixed. Their assumptions are different which matter when the population gets large.
Mass action assumes transmission is unbounded when the number of infected people grows.
dS/dt = - beta IS
Caveat: beta typically has to be changed when N is changed.
Standard incidence assumes transmisson depends on the number of contacts with infected individuals and is bounded when N grows.
dS/dt = - beta/N IS
Note that bata/N is automatically adapted when N changes.
Some modes rely on propotions instead of cardinalities. For the sake of condistency it is assumed, in JKendrick, that they are reformulated in terms of quotients of cardinalities
For instance the SIR example below has these values
Initial_Susceptibles 0.999999
Initial_Infecteds 0.000001
This is reformulated as
N = 1 000 000
S = 999 999
I = 1
and implicitly
R = 0
STATUS : not yet enforced ^^
The transition rates matrix is composed of the functional transitions rates between compartments. A transition is set by giving the compartment from which the transition starts, the compartment toward which it ends, and the rate at which the transition occurs. Each concern has it’s own transition rates matrix.
When concerns are merged into a scenario, a new transition rates matrix is created using the tensor sum of the concern’s matrices. (Not yet implemented)
Each not null transition of the matrix is considered as a possible event for stochastic simulations. For deterministic simulations, the equations are generated using the transition rates matrix.
The example is adapted from https://homepages.warwick.ac.uk/~masfz/ModelingInfectiousDiseases/Chapter2/Program_2.1/index.html.
https://github.com/KendrickOrg/kendrick/wiki/Basic-SIR-(Benchmark-Model-1)
https://github.com/KendrickOrg/kendrick/blob/ba11051d2c8976bb27a12da0e3e51650cee99e51/src/Kendrick-Examples/KEDeterministicExamples.class.st#L1183
