surveillance (version 1.2-1)

algo.hhh: Model fit based on the Held, Hoehle, Hofman paper

Description

Fits a Poisson/negative binomial model with mean $\mu_{it}$ (as described in Held/H�hle{Hoehle}/Hofmann, 2005) to a multivariate time series of counts.

Usage

algo.hhh(disProgObj, control=list(lambda=TRUE, neighbours=FALSE, 
   linear=FALSE, nseason = 0,
   negbin=c("none", "single", "multiple"), 
   proportion=c("none", "single", "multiple"),lag.range=NULL), 
   thetastart=NULL, verbose=TRUE)

Arguments

disProgObj

object of class disProg

control

control object: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

thetastart

vector with starting values for all parameters specified in the control object (for optim).

verbose

if true information about convergence is printed

Value

• ahg
  Returns an object of class ah with elements

  • coefficients

  {estimated parameters} se{estimated standard errors} cov{covariance matrix} loglikelihood{loglikelihood} convergence{logical indicating whether optim converged or not} fitted.values{fitted mean values $\mu_{i,t}$ } control{specified control object} disProgObj{specified disProg-object} lag{which lag was used for the autoregressive parameters $lambda$ and $phi$ } nObs{number of observations used for fitting the model}

encoding

latin1

source

Held, L., H�hle{Hoehle}, M., Hofmann, M. (2005) A statistical framework for the analysis of multivariate infectious disease surveillance counts. Statistical Modelling, 5, p. 187--199.

Details

Note that for the time being this function is not a surveillance algorithm, but only a modelling approach as described in the Held et. al (2005) paper.

See Also

meanResponse

Examples

# univariate time series: salmonella agona cases 
data(salmonella.agona)
salmonella <- create.disProg(week=1:length(salmonella.agona$observed),
                             observed=salmonella.agona$observed, 
                             state=salmonella.agona$state)

model1 <- list(lambda=TRUE, linear=TRUE, 
               nseason=1, negbin="single")
					
algo.hhh(salmonella, control=model1)

# multivariate time series: 
# measles cases in Lower Saxony, Germany
data(measles.weser)

# same model as above
algo.hhh(measles.weser, control=model1)

# different starting values for 
# theta = (lambda, beta, gamma_1, gamma_2, psi)
startValues <- c(0.1, rep(0, 3), 1)
algo.hhh(measles.weser, control=model1, 
                 thetastart=startValues)

# include autoregressive parameter phi for adjacent "Kreise"
model2 <- list(lambda=TRUE, neighbours=TRUE, 
               linear=FALSE, nseason=1, 
               negbin="single")
algo.hhh(measles.weser, control=model2)
                  
## weekly counts of influenza and meningococcal infections 
## in Germany, 2001-2006
data(influMen)

# specify model with two autoregressive parameters lambda_i, overdispersion
# parameters psi_i, an autoregressive parameter phi for meningococcal infections
# (i.e. nu_flu,t = lambda_flu * y_flu,t-1  
#  and  nu_men,t = lambda_men * y_men,t-1 + phi_men*y_flu,t-1 )
# and S=(3,1) Fourier frequencies
model <- list(lambda=c(TRUE,TRUE), neighbours=c(FALSE,TRUE),
              linear=FALSE,nseason=c(3,1),negbin="multiple")
              
# run algo.hhh
algo.hhh(influMen, control=model)

# now meningococcal infections in the same week should enter as covariates
# (i.e. nu_flu,t = lambda_flu * y_flu,t-1  
#  and  nu_men,t = lambda_men * y_men,t-1 + phi_men*y_flu,t )
model2 <- list(lambda=c(1,1), neighbours=c(NA,0),
              linear=FALSE,nseason=c(3,1),negbin="multiple")
              
algo.hhh(influMen, control=model2)