Dear Massimo This site have some code that may help you to get started. http://www.statistik.lmu.de/~hoehle/software/
Also if you want to take an agent-based approach, you may want to take a look at the "simecol" package Regards, Francisco Massimo Fenati wrote: > Thanks for your fast advises. > > A simple examples of SEIR model is shown below: > > #expample of very simple SEIR model##### > library(odesolve) > times<-seq(0,1200,1) > parms<-c( > b=0.35, #BETA OR COEFFICIENT OF TRANSMISSION > pl=1/7, #LATENCY > g=1/21, #RECOVERY > a=0.05/7 #LETHALITY IN ADULT > ) > xstart<-c(S=100,E=0,I=1,R=0,nn=101) > model<-function(t,x,parms){ > S <-x[1] > E <-x[2] > I <-x[3] > R <-x[4] > nn <-x[5] > > with(as.list(parms),{ > dS<--S*b*I/nn > dE<-S*b*I/nn-E*pl > dI<-E*pl-I*(a+g) > dR<-I*g > dnn<-dS+dE+dI+dR > > list(c(dS,dE,dI,dR,dnn)) > }) > } > out<-as.data.frame(lsoda(xstart,times,model,parms)) > plot(times,out$S,type="l") > lines(times,out$E,col="green") > lines(times,out$I,col="red") > lines(times,out$R,col="blue") > ######### > > I'd like to vary one or more parameters (with several > distribution) for obtaining probabilistic results from the > model projections. > > Now I'll try also with winBUGS, but I've never worked with > it. I hope.. > > Thank you very much. > > Max > > > > On Thu, 16 Nov 2006 09:26:12 -0500 > Tamas K Papp <[EMAIL PROTECTED]> wrote: >> On Thu, Nov 16, 2006 at 02:55:07PM +0100, Massimo Fenati >> wrote: >>> Dear colleagues, >>> I’m a new R-help user. I’ve read the advertisements >>> about >>> the good manners and I hope to propose a good question. >>> I’m using R to build an epidemiological SEIR model based >>> on ODEs. The odesolve package is very useful to solve >>> deterministic ODE systems but I’d like to perform a >>> stochastic simulation based on Markov chain Montecarlo >>> methods. I don’t know which packages could be used to do >>> it (I tried with "sde" but without results). >>> Have you some suggestions about useful methods and/or >>> function in R for reaching my aim. >> Hi Max, >> >> I don't know what SEIR is. R has some MCMC packages >> (RSiteSearch("MCMC") will help you there), but it is >> easy to write a >> Metropolis/Gibbs sampler yourself, making use of the >> structure of your >> problem. >> >> The sde package is for approximating likelihoods of >> continuous time >> stochastic differential equations (which is a difficult >> problem by >> itself). >> >> It is hard to give more advice without knowing what you >> are trying to >> achieve -- it is good that you have read the posting >> guide, but please >> give more details if you want more specific answers. >> >> HTH, >> >> Tamas > > MASSIMO FENATI > ----------------------------------------- > Istituto Nazionale per la Fauna Selvatica > Via Cà Fornacetta, 9 > 40064 - Ozzano dell'Emilia (BO)- Italy > tel: +39 0516512245 > cel: +39 3392114911 > fax: +39 051796628 > e-mail: [EMAIL PROTECTED] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Dr. Francisco J. Zagmutt College of Veterinary Medicine and Biomedical Sciences Colorado State University ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.