Description Usage Arguments Details Value References Examples

Runs an MCMC algorithm for the estimation of specified model parameters

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
epimcmc (type, x = NULL, y = NULL, inftime, tmin = NULL, tmax, infperiod = NULL,
niter, alphaini, betaini, sparkini = NULL, Sformula = NULL,
contact = NULL, pro.var.a, pro.var.b, pro.var.sp = NULL, prioralpha,
halfnorm.var.a = NULL, gamma.par.a = NULL, unif.range.a = NULL, priorbeta,
halfnorm.var.b = NULL, gamma.par.b = NULL, unif.range.b = NULL,
priorsp = NULL, halfnorm.var.sp = NULL, gamma.par.sp = NULL,
unif.range.sp = NULL, tempseed = NULL)
``` |

`type` |
Type of compartment framework, with the choice of "SI" for Susceptible-Infectious diseases and "SIR" for Susceptible-Infectious-Removed |

`x` |
X coordinates of individuals |

`y ` |
Y coordinates of individuals |

`inftime ` |
Times at which individuals are infected to initialize epidemic simulation |

`tmin` |
The first time point at which the infection occurs, default value is one |

`tmax` |
The last time point at which data is observed |

`infperiod` |
Length of infectious period for each individual |

`niter ` |
Number of MCMC iterations |

`alphaini` |
Initial value of susceptibility parameter(s)(>0) |

`betaini` |
Initial value of spatial parameter(s) (>0) or network parameter (s) (>0) if contact is used |

`sparkini` |
Initial value of spark parameter (>=0) |

`Sformula` |
An object of class formula. See formula Individual-level covariate information associated with susceptibility can be passed through this argument. An expression of the form |

`contact` |
Contact network matrix (matrices) |

`pro.var.a` |
Proposal density variance for alpha parameter(s) |

`pro.var.b` |
Proposal density variance for beta parameter(s) |

`pro.var.sp` |
Proposal density variance for spark parameter |

`prioralpha` |
Select the prior distribution for alpha parameter(s) with the choice of "halfnormal" for positive half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |

`halfnorm.var.a` |
Half normal prior variance for alpha |

`gamma.par.a` |
Gamma prior: shape and rate parameters for alpha |

`unif.range.a` |
Uniform prior: Minimum and maximum range for alpha |

`priorbeta` |
Select the prior distribution for beta parameter(s) with the choice of "halfnormal" for half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |

`halfnorm.var.b` |
Half normal prior variance for beta |

`gamma.par.b` |
Gamma prior: shape and rate parameters for beta |

`unif.range.b` |
Uniform prior: Minimum and maximum range for beta |

`priorsp` |
Select the prior distribution for spark parameter with the choice of "halfnormal" for half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |

`halfnorm.var.sp` |
Half normal prior variance for spark |

`gamma.par.sp` |
Gamma prior: shape and rate parameters for spark |

`unif.range.sp` |
Uniform prior: Minimum and maximum range for spark |

`tempseed` |
Integer seed value to initialize the (Fortran) random number generator, default value is a random seed. |

Independent Gaussian random walks are used as the Metropolis-Hastings MCMC proposal for all parameters

A list is returned with the following components:

`Estimates ` |
MCMC output as coda object |

`Loglikelihood ` |
Log likelihood value of each posterior estimate |

Rob Deardon, Xuan Fang, and Grace P. S. Kwong (2014).
Statistical modelling of spatio-temporal infectious disease tranmission in Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases,
*(Ed: D. Chen, B. Moulin, J. Wu), John Wiley & Sons.*. Chapter 11.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## Example 1: spatial SI model
# generate 100 individuals
## Not run:
x <- runif(100, 0, 10)
y <- runif(100, 0, 10)
covariate <- runif(100, 0, 2)
out <- epidata(type = "SI", n = 100, Sformula = ~covariate, tmax = 15,
alpha = c(0.1, 0.3), beta = 5.0, x = x, y = y)
mcout <- epimcmc(type = "SI", x = x, y = y, inftime = out$inftime, Sformula = ~covariate,
tmax = 15, niter = 1000, alphaini = c(0.01, 0.01), betaini = 0.01,
pro.var.a = c(0.01, 0.005), pro.var.b = 0.05,
prioralpha = "halfnormal", halfnorm.var.a = c(10**5, 10**5),
priorbeta = "halfnormal", halfnorm.var.b = 10**5)
## End(Not run)
``` |

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