A fast, flexible agent-based disease modeling framework

You can easily customize model parameters, and run simulations in parallel:

1. Create a dictionary defining the parameters of the simulation.
2. Modify only those parameters you want to differ between scenarios.
3. Run the simulations in parallel, and plot the results you are interested in.

import starsim as ss
import sciris as sc

# Set the parameters for the baseline simulation
pars1 = sc.objdict( # Note: can also use regular Python dictionary
	n_agents = 10_000,     # Number of agents to simulate
	networks = sc.objdict( # *Networks* add detail on how the agents interact with each other
		type = 'random',   # Here, we use a 'random' network
		n_contacts = 4     # Each person has an average of 4 contacts with other people
	),
	diseases = sc.objdict( # *Diseases* add detail on what diseases to model
		type = 'sis',      # Here, we're creating an SIS disease
		init_prev = 0.1,   # Proportion of the population initially infected
		beta = 0.1,        # Probability of transmission between contacts
	)
)

# Make a modified version of the parameters for the scenario
pars2 = pars1.copy(deep=True)
pars2.diseases.beta = 0.2

# Create the simulations
s1 = ss.Sim(pars1, label='Low transmission')
s2 = ss.Sim(pars2, label='High transmission')

# Run and plot the simulations
msim = ss.parallel(s1, s2)
msim.plot('sis_n_infected')

Everything in Starsim can be customized, including diseases, demographics, and intervention. This example shows how to write custom interventions, namely a vaccine product and vaccination campaign:

import starsim as ss
import matplotlib.pyplot as plt

# Define the simulation parameters
pars = dict(
	n_agents = 20_000,
	birth_rate = 20,
	death_rate = 15,
	networks = dict(
		type = 'random',
		n_contacts = 4
	),
	diseases = dict(
		type = 'sir',
		dur_inf = 10,
		beta = 0.1,
	)
)

# Create the product: a vaccine with 50% efficacy
my_vaccine = ss.sir_vaccine(efficacy=0.5)

# Create the vaccine campaign
campaign = ss.routine_vx(
	start_year = 2015,    # Begin vaccination in 2015
	prob = 0.2,           # 20% coverage
	product = my_vaccine  # Use the MyVaccine product
)

# Now create two sims: a baseline sim and one with the intervention
sim_base = ss.Sim(pars=pars)
sim_intv = ss.Sim(pars=pars, interventions=campaign)

# Run sims in parallel
sims = ss.parallel(sim_base, sim_intv).sims
base = sims[0].results
vax = sims[1].results

# Plot
plt.figure()
plt.plot(base.yearvec, base.sir.prevalence, label='Baseline')
plt.plot(vax.yearvec, vax.sir.prevalence, label='Vaccine')
plt.axvline(x=2015, color='k', ls='--')
plt.title('Vaccine impact')
plt.xlabel('Year')
plt.ylabel('Prevalence')
plt.legend()

Starsim can be run from R just as easily as from Python:

# Load Starsim
library(starsim)
load_starsim()

# Set the simulation parameters
pars <- list(
	n_agents = 10000,
	birth_rate = 20,
	death_rate = 15,
	networks = list(
		type = 'randomnet',
		n_contacts = 4
	),
	diseases = list(
		type = 'sir',
		dur_inf = 10,
		beta = 0.1
	)
)

# Create, run, and plot the simulation
sim <- ss$Sim(pars)
sim$run()
sim$diseases$sir$plot()