In fintzij/BDAepimodel: Fit Stochastic Epidemic Models in R via Bayesian Data Augmentation
library(BDAepimodel)
library(coda)
library(Rcpp)
library(pomp)
library(gtools)
library(ggplot2)
Analyzing an epidemic with SIR dynamics
In this vignette, we will demonstrate how to implement the Particle Marginal Metropolis-Hastings (PMMH) algorithm (which has been implemented in the pomp package) for analyzing simulated prevalence counts. We fit SIR models to binomially distributed prevalence counts.
We simulated an epidemic with SIR dynamics in a population of 750 individuals, 90% of whom were initially susceptible, 3% of whom were initially infected, and 7% of whom were immune. The per–contact infectivity rate was $\beta = 0.00035$ and the average infectious period duration was $1/\mu = 7$ days, which together correspond to a basic reproduction number of $R_0 = \beta N / \mu = 1.84$. Binomially sampled prevalence was observed at one week intervals over a period of three months with sampling probability $\rho=0.2$.
The data simulation code is in "SIR_SEIR_SIRS" vignettes. And the data is shown below.
data<-c(7,2,4,14,14,20,21,8,7,6,1,2,2,0,0,1)
Using Approximate Tau-leaping Algorithm:
We use the SIR model to model the dataset. The hosts are divided into three classes, according to their status. The susceptible class (S) contains those that have not yet been infected and are thereby still susceptible to it; the infected class (I) comprises those who are currently infected and, by assumption, infectious; the removed class (R) includes those who are recovered or quarantined as a result of the infection. Individuals in R are assumed to be immune against reinfection. It is natural to formulate this model as a continuous-time Markov process. To start with, we need to specify the measurement model.
rmeas<-"
cases=rbinom(I,exp(rho)/(1+exp(rho)));//represent the data
"
dmeas<-"
lik=dbinom(cases,I,exp(rho)/(1+exp(rho)),give_log); //return the loglikelihood
"
Here, we are using the $\textbf{cases}$ to refer to the data (number of reported cases) and $\textbf{I}$ to refer to the true incidence over the reporting interval. The binomial simulator rbinom and density function dbinom are provided by R. Notice that, in these snippets, we never declare the variables; pomp will ensure tht the state variable ($\textbf{I}$), observables ($\textbf{cases}$), parameters ($\textbf{rho}$, $\textbf{phi}$) and likelihood ($\textbf{lik}$) are defined in the contexts within which these snippets are executed.
And for the transition of the SIR model, we use a approximating tau-leaping algorithm, one version of which is implemented in the pomp package via the $\bf{euler.sim}$ plug-in. The algorithm holds the transition rates constant over a small interval of time and simulates the numbers of transitions that occur over the interval. The functions $\textbf{reulermultinom}$ draw random deviates from such distributions. Then we need to first specify a function that advances the states from $t$ to $t+\triangle t$, and we give the trantition of the SIR model code below:
sir.step<-"
double rate[2];
double dN[2];
rate[0]=exp(beta)*I; //Infection rate
rate[1]=exp(mu); //recovery rate
reulermultinom(1,S,&rate[0],dt,&dN[0]); //generate the number of newly infected people
reulermultinom(1,I,&rate[1],dt,&dN[1]); //generate the number of newly recovered people
if(!R_FINITE(S)) Rprintf(\"%lg %lg %lg %lg %lg %lg %lg %lg %lg\\n\",dN[0],rate[0],dN[1],rate[1],beta,mu,S,I,R);
S+=-dN[0]; //update the number of Susceptible
I+=dN[0]-dN[1]; //update the number of Infection
R+=dN[1]; //update the number of Recovery
"
The pomp will ensure that the undeclared state variables and parameters are defined in the context within which the snippet is executed. And the $\textbf{rate}$ and $\textbf{dN}$ arrays hold the rates and numbers of transition events, respectively. Then we could construct the pomp objects below:
sir <- pomp(
data = data.frame(cases = data, time = seq(1, 106, by = 7)), #"cases" is the dataset, "time" is the observation time
times = "time",
t0 = 1, #initial time point
dmeasure = Csnippet(dmeas),
rmeasure = Csnippet(rmeas), #return the likelihood
rprocess = euler.sim(step.fun = Csnippet(sir.step), delta.t = 1/12), #delta.t is here
statenames = c("S", "I", "R"), #state space variable name
paramnames = c("beta", "mu", "rho", "theta1", "theta2"), #parameters name
initializer = function(params, t0, ...) {
ps <-exp(params["theta1"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of susceptible
pi <- 1 / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of infection
pr <-exp(params["theta2"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of recovery
return(setNames(as.numeric(rmultinom(
1, 750, prob = c(ps, pi, 1 - ps - pi)
)), c("S", "I", "R")))
},
params = c( #for data generation(We didn't generate data here, but pomp need this part.)
beta = -5,
mu = -2,
rho = 0,
theta1 = 6,
theta2 = 1
)
)
To carry out Bayesian inference we need to specify a prior distribution on unknown parame- ters. The pomp constructor function provides the rprior and dprior arguments, which can be filled with functions that simulate from and evaluate the prior density, respectively. Methods based on random walk Metropolis-Hastings require evaluation of the prior density (dprior), so we specify dprior for the SIR model as follows.
sir.dprior <- function(params, ..., log) {
f <- dgamma(exp(params[1]), shape = 0.3, rate = 1000, log = TRUE) + params[1] + # log prior for log(infection rate) "beta"
dgamma(exp(params[2]), shape = 1, rate = 8, log = TRUE) + params[2] + # log prior for log(recovery rate) "mu"
dbeta(exp(params[3]) / (1 + exp(params[3])), 2, 7, log = TRUE) +
params[3] - log((1 + exp(params[3])) ^ 2) + #log prior for logit(sampling probablity) "rho"
log(ddirichlet(c(exp(params[4]) / (1 + exp(params[4]) + exp(params[5])),
1 / (1 + exp(params[4]) + exp(params[5])),
exp(params[5]) / (1 + exp(params[4]) + exp(params[5]))), c(90, 2, 5))) +
params[4] + params[5] - 3 * log(1 + exp(params[4]) + exp(params[5])) #log prior for logit(initial value)
if (log) {
f
} else{
exp(f)
}
}
And starting value of the PMMH is specified below:
param.initial <- c(
beta = log(rnorm(1, 0.00035, 1e-5)),
mu = log(rnorm(1, 1/7, 1e-2)),
rho = -1,
theta1 = 4,
theta2 = 1
)
And the following runs 1 PMMH chain for 5000 tunning steps (using 100 particles) to get a suitable multivariate normal random walk proposal diagonal variance-covariance matrix value.
pmcmc1 <- pmcmc(
pomp(sir, dprior = sir.dprior),
#given the prior function
start = param.initial,
#given the initial value of the parameters
Nmcmc = 10,
#number of mcmc steps
Np = 100,
max.fail = Inf,
proposal = mvn.rw.adaptive(0.1 * c(
beta = 0.2,
#sampling variance for beta
mu = 0.2,
#sampling variance for mu
rho = 0.3,
#sampling variance for rho
theta1 = 0.2,
#sampling variance for theta1
theta2 = 0.5 #sampling variance for theta2
),
scale.start = 100,
shape.start = 100)
)
And the following runs 1 PMMH chain for 100000 steps (using 100 particles) with suitable multivariate normal random walk proposal diagonal variance-covariance matrix value we get above to get the posterior distribution of the interested parameters.
start_time <- Sys.time(); #calculation of time
pmcmc1 <- pmcmc(
pmcmc1,
#given the prior function
start = param.initial,
#given the initial value of the parameters
Nmcmc = 10,
max.fail = Inf,
proposal = mvn.rw(covmat(pmcmc1))
)
end_time <- Sys.time(); #calculation of time
run_time <- difftime(end_time, start_time, units = "hours") #calculation of time
pomp_results <- list(time = run_time, results = pmcmc1)
Using Exact Gillespie Algorithm:
We use the SIR model to model the dataset. The hosts are divided into three classes, according to their status. The susceptible class (S) contains those that have not yet been infected and are thereby still susceptible to it; the infected class (I) comprises those who are currently infected and, by assumption, infectious; the removed class (R) includes those who are recovered or quarantined as a result of the infection. Individuals in R are assumed to be immune against reinfection. It is natural to formulate this model as a continuous-time Markov process. To start with, we need to specify the measurement model.
rmeas<-"
cases=rbinom(I,exp(rho)/(1+exp(rho)));//represent the data
"
dmeas<-"
lik=dbinom(cases,I,exp(rho)/(1+exp(rho)),give_log); //return the loglikelihood
"
Here, we are using the $\textbf{cases}$ to refer to the data (number of reported cases) and $\textbf{I}$ to refer to the true incidence over the reporting interval. The binomial simulator rbinom and density function dbinom are provided by R. Notice that, in these snippets, we never declare the variables; pomp will ensure tht the state variable ($\textbf{I}$), observables ($\textbf{cases}$), parameters ($\textbf{rho}$, $\textbf{phi}$) and likelihood ($\textbf{lik}$) are defined in the contexts within which these snippets are executed.
And for the transition of the SIR model, we use an exact gillespie algorithm, one version of which is implemented in the pomp package via the $\bf{gillespie.sim}$ plug-in. We give the trantition of the SIR model code below:
# define the rate function, stoichiometry matrix, and rate-event dependency matrix
SIR_stoich <- cbind(infection = c(-1, 1, 0), # infections yield S-1, I+1
recovery = c(0, -1, 1)) # recoveries yield I-1, R+1
SIR_depmat <- cbind(infection = c(1, 1, 0), # infectivity rate updated by changes to S and I
recovery = c(0, 1, 0)) # recovery rate updated by changes to I
# SIR rate function
# j the number of the elementary event (1 = infection, 2 = recovery)
# x named numeric vector with the value of the state of the process at time t
# t time
# params named numeric vector containing the parameters
# returns single numerical value with the rate of the elementary event
SIR_rates <- function(j, x, t, params, ...) {
switch(j,
exp(params["beta"]) * x["S"] * x["I"], # infection
exp(params["mu"]) * x["I"] # recovery
)
}
# instatiate the gillespie stepper function
SIR_sim <- gillespie.sim(rate.fun = SIR_rates,
v = SIR_stoich,
d = SIR_depmat)
The pomp will ensure that the undeclared state variables and parameters are defined in the context within which the snippet is executed. And the $\textbf{rate}$ and $\textbf{dN}$ arrays hold the rates and numbers of transition events, respectively. Then we could construct the pomp objects below:
sir <- pomp(
data = data.frame(time = seq(1, 106, by = 7), cases = data), #"cases" is the dataset, "time" is the observation time
times = "time",
t0 = 1, # initial time point
dmeasure = Csnippet(dmeas), # evaluates the density of the measurement process
rmeasure = Csnippet(rmeas), # simulates from the measurement process
rprocess = SIR_sim, # simulates from the latent process
statenames = c("S", "I", "R"), #state space variable name
paramnames = c("beta", "mu", "rho", "theta1", "theta2"), #parameters name
initializer = function(params, t0, ...) {
ps <-exp(params["theta1"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of susceptible
pi <- 1 / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of infection
pr <-exp(params["theta2"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of recovery
return(setNames(as.numeric(rmultinom(1, 750, prob = c(ps, pi, 1 - ps - pi))), c("S", "I", "R")))
},
params = c( #for data generation(We didn't generate data here, but pomp need this part.)
beta = -5,
mu = -2,
rho = 0,
theta1 = 6,
theta2 = 1
)
)
To carry out Bayesian inference we need to specify a prior distribution on unknown parame- ters. The pomp constructor function provides the rprior and dprior arguments, which can be filled with functions that simulate from and evaluate the prior density, respectively. Methods based on random walk Metropolis-Hastings require evaluation of the prior density (dprior), so we specify dprior for the SIR model as follows.
sir.dprior <- function(params, ..., log) {
f <- dgamma(exp(params[1]), shape = 0.3, rate = 1000, log = TRUE) + params[1] + # log prior for log(infection rate) "beta"
dgamma(exp(params[2]), shape = 1, rate = 8, log = TRUE) + params[2] + # log prior for log(recovery rate) "mu"
dbeta(exp(params[3]) / (1 + exp(params[3])), 2, 7, log = TRUE) +
params[3] - log((1 + exp(params[3])) ^ 2) + #log prior for logit(sampling probablity) "rho"
log(ddirichlet(c(exp(params[4]) / (1 + exp(params[4]) + exp(params[5])),
1 / (1 + exp(params[4]) + exp(params[5])),
exp(params[5]) / (1 + exp(params[4]) + exp(params[5]))), c(90, 2, 5))) +
params[4] + params[5] - 3 * log(1 + exp(params[4]) + exp(params[5])) #log prior for logit(initial value)
if (log) {
f
} else{
exp(f)
}
}
And starting value of the PMMH is specified below:
param.initial <- c(
beta = log(rnorm(1, 0.00035, 1e-5)),
mu = log(rnorm(1, 1/7, 1e-2)),
rho = -1,
theta1 = 4,
theta2 = 1
)
And the following runs 1 PMMH chain for 5000 tunning steps (using 200 particles) to get a suitable multivariate normal random walk proposal diagonal variance-covariance matrix value.
pmcmc1 <- pmcmc(
pomp(sir, dprior = sir.dprior),
#given the prior function
start = param.initial,
#given the initial value of the parameters
Nmcmc = 10,
#number of mcmc steps
Np = 200,
max.fail = Inf,
proposal = mvn.rw.adaptive(0.1 * c(
beta = 0.2,
#sampling variance for beta
mu = 0.2,
#sampling variance for mu
rho = 0.3,
#sampling variance for rho
theta1 = 0.2,
#sampling variance for theta1
theta2 = 0.5 #sampling variance for theta2
),
scale.start = 100,
shape.start = 100)
)
And the following runs 1 PMMH chain for 100000 steps (using 200 particles) with suitable multivariate normal random walk proposal diagonal variance-covariance matrix value we get above to get the posterior distribution of the interested parameters.
start_time <- Sys.time(); #calculation of time
pmcmc1 <- pmcmc(
pmcmc1,
#given the prior function
start = param.initial,
#given the initial value of the parameters
Nmcmc = 10,
max.fail = Inf,
proposal = mvn.rw(covmat(pmcmc1))
)
end_time <- Sys.time(); #calculation of time
run_time <- difftime(end_time, start_time, units = "hours") #calculation of time
pomp_results <- list(time = run_time, results = pmcmc1)
fintzij/BDAepimodel documentation built on Sept. 20, 2020, 1:44 p.m.
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library(BDAepimodel) library(coda) library(Rcpp) library(pomp) library(gtools) library(ggplot2)
Analyzing an epidemic with SIR dynamics
In this vignette, we will demonstrate how to implement the Particle Marginal Metropolis-Hastings (PMMH) algorithm (which has been implemented in the pomp package) for analyzing simulated prevalence counts. We fit SIR models to binomially distributed prevalence counts.
We simulated an epidemic with SIR dynamics in a population of 750 individuals, 90% of whom were initially susceptible, 3% of whom were initially infected, and 7% of whom were immune. The per–contact infectivity rate was $\beta = 0.00035$ and the average infectious period duration was $1/\mu = 7$ days, which together correspond to a basic reproduction number of $R_0 = \beta N / \mu = 1.84$. Binomially sampled prevalence was observed at one week intervals over a period of three months with sampling probability $\rho=0.2$.
The data simulation code is in "SIR_SEIR_SIRS" vignettes. And the data is shown below.
data<-c(7,2,4,14,14,20,21,8,7,6,1,2,2,0,0,1)
Using Approximate Tau-leaping Algorithm:
We use the SIR model to model the dataset. The hosts are divided into three classes, according to their status. The susceptible class (S) contains those that have not yet been infected and are thereby still susceptible to it; the infected class (I) comprises those who are currently infected and, by assumption, infectious; the removed class (R) includes those who are recovered or quarantined as a result of the infection. Individuals in R are assumed to be immune against reinfection. It is natural to formulate this model as a continuous-time Markov process. To start with, we need to specify the measurement model.
rmeas<-" cases=rbinom(I,exp(rho)/(1+exp(rho)));//represent the data " dmeas<-" lik=dbinom(cases,I,exp(rho)/(1+exp(rho)),give_log); //return the loglikelihood "
Here, we are using the $\textbf{cases}$ to refer to the data (number of reported cases) and $\textbf{I}$ to refer to the true incidence over the reporting interval. The binomial simulator rbinom and density function dbinom are provided by R. Notice that, in these snippets, we never declare the variables; pomp will ensure tht the state variable ($\textbf{I}$), observables ($\textbf{cases}$), parameters ($\textbf{rho}$, $\textbf{phi}$) and likelihood ($\textbf{lik}$) are defined in the contexts within which these snippets are executed.
And for the transition of the SIR model, we use a approximating tau-leaping algorithm, one version of which is implemented in the pomp package via the $\bf{euler.sim}$ plug-in. The algorithm holds the transition rates constant over a small interval of time and simulates the numbers of transitions that occur over the interval. The functions $\textbf{reulermultinom}$ draw random deviates from such distributions. Then we need to first specify a function that advances the states from $t$ to $t+\triangle t$, and we give the trantition of the SIR model code below:
sir.step<-" double rate[2]; double dN[2]; rate[0]=exp(beta)*I; //Infection rate rate[1]=exp(mu); //recovery rate reulermultinom(1,S,&rate[0],dt,&dN[0]); //generate the number of newly infected people reulermultinom(1,I,&rate[1],dt,&dN[1]); //generate the number of newly recovered people if(!R_FINITE(S)) Rprintf(\"%lg %lg %lg %lg %lg %lg %lg %lg %lg\\n\",dN[0],rate[0],dN[1],rate[1],beta,mu,S,I,R); S+=-dN[0]; //update the number of Susceptible I+=dN[0]-dN[1]; //update the number of Infection R+=dN[1]; //update the number of Recovery "
The pomp will ensure that the undeclared state variables and parameters are defined in the context within which the snippet is executed. And the $\textbf{rate}$ and $\textbf{dN}$ arrays hold the rates and numbers of transition events, respectively. Then we could construct the pomp objects below:
sir <- pomp( data = data.frame(cases = data, time = seq(1, 106, by = 7)), #"cases" is the dataset, "time" is the observation time times = "time", t0 = 1, #initial time point dmeasure = Csnippet(dmeas), rmeasure = Csnippet(rmeas), #return the likelihood rprocess = euler.sim(step.fun = Csnippet(sir.step), delta.t = 1/12), #delta.t is here statenames = c("S", "I", "R"), #state space variable name paramnames = c("beta", "mu", "rho", "theta1", "theta2"), #parameters name initializer = function(params, t0, ...) { ps <-exp(params["theta1"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of susceptible pi <- 1 / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of infection pr <-exp(params["theta2"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of recovery return(setNames(as.numeric(rmultinom( 1, 750, prob = c(ps, pi, 1 - ps - pi) )), c("S", "I", "R"))) }, params = c( #for data generation(We didn't generate data here, but pomp need this part.) beta = -5, mu = -2, rho = 0, theta1 = 6, theta2 = 1 ) )
To carry out Bayesian inference we need to specify a prior distribution on unknown parame- ters. The pomp constructor function provides the rprior and dprior arguments, which can be filled with functions that simulate from and evaluate the prior density, respectively. Methods based on random walk Metropolis-Hastings require evaluation of the prior density (dprior), so we specify dprior for the SIR model as follows.
sir.dprior <- function(params, ..., log) { f <- dgamma(exp(params[1]), shape = 0.3, rate = 1000, log = TRUE) + params[1] + # log prior for log(infection rate) "beta" dgamma(exp(params[2]), shape = 1, rate = 8, log = TRUE) + params[2] + # log prior for log(recovery rate) "mu" dbeta(exp(params[3]) / (1 + exp(params[3])), 2, 7, log = TRUE) + params[3] - log((1 + exp(params[3])) ^ 2) + #log prior for logit(sampling probablity) "rho" log(ddirichlet(c(exp(params[4]) / (1 + exp(params[4]) + exp(params[5])), 1 / (1 + exp(params[4]) + exp(params[5])), exp(params[5]) / (1 + exp(params[4]) + exp(params[5]))), c(90, 2, 5))) + params[4] + params[5] - 3 * log(1 + exp(params[4]) + exp(params[5])) #log prior for logit(initial value) if (log) { f } else{ exp(f) } }
And starting value of the PMMH is specified below:
param.initial <- c( beta = log(rnorm(1, 0.00035, 1e-5)), mu = log(rnorm(1, 1/7, 1e-2)), rho = -1, theta1 = 4, theta2 = 1 )
And the following runs 1 PMMH chain for 5000 tunning steps (using 100 particles) to get a suitable multivariate normal random walk proposal diagonal variance-covariance matrix value.
pmcmc1 <- pmcmc( pomp(sir, dprior = sir.dprior), #given the prior function start = param.initial, #given the initial value of the parameters Nmcmc = 10, #number of mcmc steps Np = 100, max.fail = Inf, proposal = mvn.rw.adaptive(0.1 * c( beta = 0.2, #sampling variance for beta mu = 0.2, #sampling variance for mu rho = 0.3, #sampling variance for rho theta1 = 0.2, #sampling variance for theta1 theta2 = 0.5 #sampling variance for theta2 ), scale.start = 100, shape.start = 100) )
And the following runs 1 PMMH chain for 100000 steps (using 100 particles) with suitable multivariate normal random walk proposal diagonal variance-covariance matrix value we get above to get the posterior distribution of the interested parameters.
start_time <- Sys.time(); #calculation of time pmcmc1 <- pmcmc( pmcmc1, #given the prior function start = param.initial, #given the initial value of the parameters Nmcmc = 10, max.fail = Inf, proposal = mvn.rw(covmat(pmcmc1)) ) end_time <- Sys.time(); #calculation of time run_time <- difftime(end_time, start_time, units = "hours") #calculation of time pomp_results <- list(time = run_time, results = pmcmc1)
Using Exact Gillespie Algorithm:
We use the SIR model to model the dataset. The hosts are divided into three classes, according to their status. The susceptible class (S) contains those that have not yet been infected and are thereby still susceptible to it; the infected class (I) comprises those who are currently infected and, by assumption, infectious; the removed class (R) includes those who are recovered or quarantined as a result of the infection. Individuals in R are assumed to be immune against reinfection. It is natural to formulate this model as a continuous-time Markov process. To start with, we need to specify the measurement model.
rmeas<-" cases=rbinom(I,exp(rho)/(1+exp(rho)));//represent the data " dmeas<-" lik=dbinom(cases,I,exp(rho)/(1+exp(rho)),give_log); //return the loglikelihood "
Here, we are using the $\textbf{cases}$ to refer to the data (number of reported cases) and $\textbf{I}$ to refer to the true incidence over the reporting interval. The binomial simulator rbinom and density function dbinom are provided by R. Notice that, in these snippets, we never declare the variables; pomp will ensure tht the state variable ($\textbf{I}$), observables ($\textbf{cases}$), parameters ($\textbf{rho}$, $\textbf{phi}$) and likelihood ($\textbf{lik}$) are defined in the contexts within which these snippets are executed.
And for the transition of the SIR model, we use an exact gillespie algorithm, one version of which is implemented in the pomp package via the $\bf{gillespie.sim}$ plug-in. We give the trantition of the SIR model code below:
# define the rate function, stoichiometry matrix, and rate-event dependency matrix SIR_stoich <- cbind(infection = c(-1, 1, 0), # infections yield S-1, I+1 recovery = c(0, -1, 1)) # recoveries yield I-1, R+1 SIR_depmat <- cbind(infection = c(1, 1, 0), # infectivity rate updated by changes to S and I recovery = c(0, 1, 0)) # recovery rate updated by changes to I # SIR rate function # j the number of the elementary event (1 = infection, 2 = recovery) # x named numeric vector with the value of the state of the process at time t # t time # params named numeric vector containing the parameters # returns single numerical value with the rate of the elementary event SIR_rates <- function(j, x, t, params, ...) { switch(j, exp(params["beta"]) * x["S"] * x["I"], # infection exp(params["mu"]) * x["I"] # recovery ) } # instatiate the gillespie stepper function SIR_sim <- gillespie.sim(rate.fun = SIR_rates, v = SIR_stoich, d = SIR_depmat)
The pomp will ensure that the undeclared state variables and parameters are defined in the context within which the snippet is executed. And the $\textbf{rate}$ and $\textbf{dN}$ arrays hold the rates and numbers of transition events, respectively. Then we could construct the pomp objects below:
sir <- pomp( data = data.frame(time = seq(1, 106, by = 7), cases = data), #"cases" is the dataset, "time" is the observation time times = "time", t0 = 1, # initial time point dmeasure = Csnippet(dmeas), # evaluates the density of the measurement process rmeasure = Csnippet(rmeas), # simulates from the measurement process rprocess = SIR_sim, # simulates from the latent process statenames = c("S", "I", "R"), #state space variable name paramnames = c("beta", "mu", "rho", "theta1", "theta2"), #parameters name initializer = function(params, t0, ...) { ps <-exp(params["theta1"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of susceptible pi <- 1 / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of infection pr <-exp(params["theta2"]) / (1 + exp(params["theta1"]) + exp(params["theta2"])) # initial prob of recovery return(setNames(as.numeric(rmultinom(1, 750, prob = c(ps, pi, 1 - ps - pi))), c("S", "I", "R"))) }, params = c( #for data generation(We didn't generate data here, but pomp need this part.) beta = -5, mu = -2, rho = 0, theta1 = 6, theta2 = 1 ) )
To carry out Bayesian inference we need to specify a prior distribution on unknown parame- ters. The pomp constructor function provides the rprior and dprior arguments, which can be filled with functions that simulate from and evaluate the prior density, respectively. Methods based on random walk Metropolis-Hastings require evaluation of the prior density (dprior), so we specify dprior for the SIR model as follows.
sir.dprior <- function(params, ..., log) { f <- dgamma(exp(params[1]), shape = 0.3, rate = 1000, log = TRUE) + params[1] + # log prior for log(infection rate) "beta" dgamma(exp(params[2]), shape = 1, rate = 8, log = TRUE) + params[2] + # log prior for log(recovery rate) "mu" dbeta(exp(params[3]) / (1 + exp(params[3])), 2, 7, log = TRUE) + params[3] - log((1 + exp(params[3])) ^ 2) + #log prior for logit(sampling probablity) "rho" log(ddirichlet(c(exp(params[4]) / (1 + exp(params[4]) + exp(params[5])), 1 / (1 + exp(params[4]) + exp(params[5])), exp(params[5]) / (1 + exp(params[4]) + exp(params[5]))), c(90, 2, 5))) + params[4] + params[5] - 3 * log(1 + exp(params[4]) + exp(params[5])) #log prior for logit(initial value) if (log) { f } else{ exp(f) } }
And starting value of the PMMH is specified below:
param.initial <- c( beta = log(rnorm(1, 0.00035, 1e-5)), mu = log(rnorm(1, 1/7, 1e-2)), rho = -1, theta1 = 4, theta2 = 1 )
And the following runs 1 PMMH chain for 5000 tunning steps (using 200 particles) to get a suitable multivariate normal random walk proposal diagonal variance-covariance matrix value.
pmcmc1 <- pmcmc( pomp(sir, dprior = sir.dprior), #given the prior function start = param.initial, #given the initial value of the parameters Nmcmc = 10, #number of mcmc steps Np = 200, max.fail = Inf, proposal = mvn.rw.adaptive(0.1 * c( beta = 0.2, #sampling variance for beta mu = 0.2, #sampling variance for mu rho = 0.3, #sampling variance for rho theta1 = 0.2, #sampling variance for theta1 theta2 = 0.5 #sampling variance for theta2 ), scale.start = 100, shape.start = 100) )
And the following runs 1 PMMH chain for 100000 steps (using 200 particles) with suitable multivariate normal random walk proposal diagonal variance-covariance matrix value we get above to get the posterior distribution of the interested parameters.
start_time <- Sys.time(); #calculation of time pmcmc1 <- pmcmc( pmcmc1, #given the prior function start = param.initial, #given the initial value of the parameters Nmcmc = 10, max.fail = Inf, proposal = mvn.rw(covmat(pmcmc1)) ) end_time <- Sys.time(); #calculation of time run_time <- difftime(end_time, start_time, units = "hours") #calculation of time pomp_results <- list(time = run_time, results = pmcmc1)
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