Creating a more advanced System Dynamics simulation entails introducing more state variables and more complex relationships between them. Here is an example of a more sophisticated simulation involving population dynamics with births, natural deaths, and disease-related deaths, influenced by a disease prevalence rate in the population.
**Explanation:**
- **SystemDynamicsSimulation class**
- `constructor`: Sets up the simulation with an initial state and parameters governing the dynamics of the system.
- `step`: Calculates the changes in population and disease prevalence over one time step using the current rates and state variables. It introduces a new variable `newInfections` which models the spread of the disease in the population.
- `run`: Runs the simulation for a specified number of time steps, logging the state variables at each step.
- **Main function**
- We configure and initiate a simulation with a more detailed set of parameters, including rates of birth, natural death, and disease-related death, as well as disease prevalence and spread rate.
- We run the simulation for 100 time steps, logging the state at each step.
In this simulation:
- **Births** are proportional to the current population.
- **Natural deaths** are proportional to the current population.
- **Disease-related deaths** are proportional to the disease prevalence and the current population.
- **Disease spread** is modeled using a simple logistic growth equation, with new infections being a function of the current disease prevalence, the susceptible population (1 - disease prevalence), and a disease spread rate.
This is a more advanced version of a system dynamics simulation, incorporating more complex relationships between state variables. Adjust the parameters and dynamics as necessary to suit your specific simulation needs. You can further extend this by adding recovery rates, introducing interventions, or including more state variables and dynamics to model more complex systems.
# Source Code Snippet:
class SystemDynamicsSimulation {
constructor({ initialPopulation, birthRate, naturalDeathRate, diseaseDeathRate, initialDiseasePrevalence, diseaseSpreadRate, timeStep }) {
this.population = initialPopulation;
this.diseasePrevalence = initialDiseasePrevalence;
this.birthRate = birthRate;
this.naturalDeathRate = naturalDeathRate;
this.diseaseDeathRate = diseaseDeathRate;
this.diseaseSpreadRate = diseaseSpreadRate;
this.timeStep = timeStep;
this.time = 0;
}
step() {
// Calculate the new births and deaths
const births = this.birthRate * this.population * this.timeStep;
const naturalDeaths = this.naturalDeathRate * this.population * this.timeStep;
const diseaseDeaths = this.diseaseDeathRate * this.diseasePrevalence * this.population * this.timeStep;
// Update the disease prevalence
const newInfections = this.diseaseSpreadRate * this.diseasePrevalence * (1 - this.diseasePrevalence) * this.timeStep;
this.diseasePrevalence += newInfections - (naturalDeaths + diseaseDeaths) / this.population;
// Update the population and time
this.population += births - naturalDeaths - diseaseDeaths;
this.time += this.timeStep;
}
run(steps) {
for (let i = 0; i < steps; i++) {
this.step();
console.log(`Time: ${this.time.toFixed(2)}, Population: ${this.population.toFixed(2)}, Disease prevalence: ${(this.diseasePrevalence * 100).toFixed(2)}%`);
}
}
}
function main() {
// Initialize the simulation with initial parameters
const simulation = new SystemDynamicsSimulation({
initialPopulation: 1000,
birthRate: 0.01,
naturalDeathRate: 0.005,
diseaseDeathRate: 0.01,
initialDiseasePrevalence: 0.1,
diseaseSpreadRate: 0.05,
timeStep: 0.1,
});
// Run the simulation for 100 time steps
simulation.run(100);
}
main();