`R/stochastic_sib_model.R`

`stochastic_sib_model.Rd`

`stochastic_sib_model`

Stochastic SIB model for infected cases simulation

stochastic_sib_model( mu, beta, rho, sigma, gamma, alpha, mu_B, m = 0.3, theta, nnodes, POP_node, fluxes, time_sim, y0 )

mu | population natality and mortality rate (day^-1) |
---|---|

beta | contact rate |

rho | immunity loss rate (day^-1) |

sigma | symptomatic ratio, i.e., fraction of infected people that develop symptoms and are infective. (The remaining fraction enters directly the recovered compartment.) |

gamma | rate at which people recover from cholera (day^-1) |

alpha | cholera induced mortality rate (day^-1) |

mu_B | death rate of V.cholerae in the aquatic environment (day^-1) |

m | parameter for infection force, default value is 0.3 |

theta | contamination rate |

nnodes | number of nodes/cities |

POP_node | vector, length represents number of cities/nodes; vector represents population at each node |

fluxes | matrix, number of nodes x number of nodes where each row contains the probabilities a person travels from the given city (by Row Index) to another city (by Column Index). |

time_sim | time steps for simulation, e.g., seq(0, 100, 0.1) |

y0 | initial condition for stochastic_sib_model, output of 'initial_condition_sib_model' |

a matrix, nnodes x number of time steps, representing number of new cases at each node, each time step

Li, J., Manitz, J., Bertuzzo, E. and Kolaczyk, E.D. (2020). Sensor-based localization of epidemic sources on human mobility networks. arXiv preprint Available online: https://arxiv.org/abs/2011.00138.

Jun Li

set.seed(2020) popu <- rep(20000, 10) sigma <- 0.05 mu_B <- 0.2 theta_max <- 16 theta <- runif(10, 0.1, 0.9) * theta_max y0 <- initial_condition_sib_model(popu, sigma, mu_B, theta, c(3)) time_sim <- seq(0, 1, by=0.1) mu <- 4e-05 beta_max <- 1 rho <- 0 beta <- runif(10, 0.1, 0.9) * beta_max gamma <- 0.2 alpha <- 0 humanmob.mass <- matrix(runif(100, 0.1, 0.9), 10, 10) diag(humanmob.mass) <- 0 for (j in 1:10) { humanmob.mass[j, ] <- humanmob.mass[j, ]/sum(humanmob.mass[j, ]) } simu.list = stochastic_sib_model(mu = mu, beta = beta, rho = rho, sigma = sigma, gamma = gamma, alpha = alpha, mu_B = mu_B, theta = theta, nnodes = 10, POP_node = popu, fluxes = humanmob.mass, time_sim = time_sim, y0 = y0)