- Time-Inhomogeneous Adult Mortality
- Time-Dependent Rate Functions
- Petri Net Setup
- Simulation of Fully Specified SPN Model
- Results
- References

The Stochastic Petri net (SPN) framework used in
**MGDrivE2** allows for different functional forms of the
hazard functions (or rates, if the model is being interpreted as a set
of ordinary differential equations, ODEs. We use the terms hazard/rate
function interchangeably). This means that time-dependent/inhomogeneous
rates are relatively simple to incorporate into the model, without even
changing the underlying Petri Net. We remark here that *exact*
simulation of time-inhomogeneous stochastic processes is difficult, and
the sampling algorithms to support such simulation have not yet been
incorporated into **MGDrivE2** (ODE simulation will, of
course, be “exact”). However, the Poisson time-step sampler provided
with **MGDrivE2** will be correct as the stochastic
time-step approaches zero, and we recommend its use for simulating
inhomogeneous, continuous-time Markov chain models. Exact algorithms for
simulating time-inhomogeneous processes can be found in the works of Anderson
and Thanh.
Future development efforts will concentrate on better exact and
approximate sampling algorithms.

We start by loading the **MGDrivE2** package, as well as
the **MGDrivE** package for access to inheritance cubes,
**ggplot2** for graphical analysis, and
**parallel** for running stochastic repetitions in
parallel.

```
# simulation functions
library(MGDrivE2)
# inheritance patterns
library(MGDrivE)
# plotting
library(ggplot2)
# parallel sims
library(parallel)
```

Let us consider the case where we want to simulate a model in which
the transitions made by functions
`make_transition_male_mort()`

and
`make_transition_female_mort()`

, that is, the adult male and
female mosquito mortality, fire at a rate which depends on time. This
time-dependence can in general be arbitrarily complex, as long as the
rate function returns a non-negative number at any positive time \(t>0\) when it is queried. Therefore
various time-inhomogeneous effects can be taken into account in
**MGDrivE2**, from long-term climactic and seasonal
changes, to rapidly oscillating changes from diurnal cycles.

Sample data included in **MGDrivE2**
(`mu_ts`

) provides estimated time-dependent adult mortality
rates from the Comoros Islands, between Mozambique and Madagascar. These
are hourly death rates over the course of an entire year, for Grande
Comore, Moheli, and Anjouan.

First, we load the `mu_ts`

object, which is a matrix of
hourly death rates. Now we want to interpolate the death rates into a
function that can return a death rate at any time in the interval \(0<t<365\). We could do this with a
variety of methods, including splines and other smoothing algorithms,
but for now we will use a simple step function with steps every hour,
created with the function `stepfun()`

.

```
# load mortality data
data(mu_ts)
# vector of hourly timesteps
# unit is in days, so 24 steps per day, 365 days
<- 1:nrow(mu_ts)/24
tt_ts
# exaggerated amplitude of Grande Comore mortality rate, for visual effect
<- mu_ts[ ,1] + ( (mu_ts[ ,1] - mean(mu_ts[ ,1]) ) * 50)
mu_ts_par
# approximating step function
<- stepfun(x = tt_ts, y = c(mu_ts_par[1], mu_ts_par),
step_approx f = 0, right = FALSE)
```

For clarity, we plot the approximating function evaluated at each hour over the year:

```
# long-form for ggplot2
<- data.frame("time" = tt_ts, "mu" = step_approx(v = tt_ts))
df_ts
# plot
ggplot(data = df_ts) +
geom_line(aes(x = time, y = mu), alpha = 0.75, color = "dodgerblue4") +
xlab("Time (days)") +
ylab("Adult Mortality") +
theme_bw() +
ggtitle("Grande Comore: Hourly Death Rates for One Year")
```

We see that the data trends in a cyclic manner, consistent with proposed seasonality throughout the year. Mosquito mortality is highest in the early spring (days 50-150, approximately March to the end of May) and around the end of the year (days 325-365, the end of November through December).

We need to setup rate functions which correctly return time-dependent
rates. First, we make a brief digression into the mathematical
consequences and then we show how to implement them into
**MGDrivE2**

Call \(\mu\) a rate function that is time-homogeneous, meaning \(\mu(t) = \mu, t \in [0,\infty)\); for a continuous-time discrete event model, this means that the time between events is exponentially distributed. Another way of saying this is that the counting process (\(N(t)\)) that counts how many times the event has “fired” (occurred) by time \(t\) is a Poisson process, that is the number of events at time \(t\) follows a Poisson distribution with parameter \(\mu \cdot t\). Consequently, if all events (transitions) have rates that are time-homogeneous functions then the entire model is a continuous-time Markov chain (CTMC). One reason we like CTMC models is that they incorporate stochasticity in a “mechanistic” way and have deep connections to classic ODE models, a mainstay of mathematical modelling. For a recent review of the relationship, using epidemic models as an example, see work by Linda Allen.

Now, let the rate function depend on time, meaning \(\mu(t) \geq 0, t \in [0,\infty)\). We need
to be careful now: while the rate function depends on *time*, it
does not depend on *age*; that is, if the event is going to the
grocery store, perhaps Alice goes to the grocery store at a different
rate each day of the week, but if the rate does not depend *at
all* on how long she has been waiting to go to the store (and has
not yet), then we have a process that is time-dependent but not
age-dependent. In other words, it is still a Markov process, but it is a
time-inhomogeneous Markov process *even though* the inter-event
times no longer follow an exponential distribution. In
**MGDrivE2** we approximate an age-dependent (semi-Markov
process) rate of egg/larvae/pupae development by using an Erlang
distribution, which is constructed by summing exponential waiting times.
If the rate parameter of the Erlang distribution varies with time, then
we can simulate time-inhomogeneous semi-Markov processes.

To make adult mortality rates time-dependent, we will need to replace
the functions `make_male_mort_haz()`

and
`make_female_mort_haz()`

with versions that return
time-dependent rate functions. First, we observe the time-inhomogeneous
rate function associated with female mortality (the function for male
mortality is very similar):

```
<- function(trans, u, cube, params, exact = TRUE, tol = 1e-8){
make_female_mort_haz
# rate constants
<- params$muF
muF
# which places have input arcs to this transition
<- trans$s
s
# weights of those arcs
<- trans$s_w
w
# omega is dependent on genotype
<- strsplit(x = u[s], split = "_", fixed = TRUE)[[1]][2]
f_gen <- cube$omega[f_gen]
omega
# return the hazard function
if(exact){
# EXACT hazards (check enabling degree: for discrete simulation only)
return(
function(trans,M){
if(w <= M[s]){
return(muF * omega * M[s])
else {
} return(0)
}
}
)
else {
}
# APPROXIMATE hazards (tolerance around zero; for continuous approximation only)
return(
function(trans,M){
<- muF * omega * M[s]
haz if(haz < tol){
return(0)
else {
} return(haz)
}
}
)
}# end of function
}
```

The rate of the adult female mosquito mortality event firing is \(\mu_{F} \cdot \omega \cdot M[s]\), where
\(M[s]\) is the number of adult female
tokens. We want to make it time dependent via \(\mu_{F}(t) \cdot \omega \cdot M[s]\), where
\(\mu(t)\) comes from our interpolating
function made with `stepfun()`

. To do so, we write a new
function, `make_female_mort_haz_inhom()`

. Note that we
preform type checking of the object `muF`

to make sure it is
indeed a function, and that the returned function closure evaluates it
at the input time `muF(t)`

.

```
<- function(trans, u, cube, params,
make_female_mort_haz_inhom exact = TRUE, tol = 1e-8){
# mortality is a time-dependent hazard
<- params$muF
muF if(typeof(muF) != "closure"){
stop("Inhomogeneous hazard 'make_female_mort_haz_inhom', ",
"'muF' in 'params' list needs to be a function")
}
# which places have input arcs to this transition
<- trans$s
s
# weights of those arcs
<- trans$s_w
w
# omega is dependent on genotype
<- strsplit(x = u[s], split = "_", fixed = TRUE)[[1]][2]
f_gen <- cube$omega[f_gen]
omega
# return the hazard function
if(exact){
# EXACT hazards (check enabling degree: for discrete simulation only)
return(
function(t,M){
if(w <= M[s]){
return(muF(t) * omega * M[s])
else {
} return(0)
}
}
)
else {
}
# APPROXIMATE hazards (tolerance around zero; for continuous approximation only)
return(
function(t,M){
<- muF(t) * omega * M[s]
haz if(haz < tol){
return(0)
else {
} return(haz)
}
}
)
}# end of function
}
```

We follow a similar procedure for the adult male mortality. The function is almost identical.

```
<- function(trans, u, cube, params,
make_male_mort_haz_inhom exact = TRUE, tol = 1e-8){
# mortality is a time-dependent hazard
<- params$muM
muM if(typeof(muM) != "closure"){
stop("Inhomogeneous hazard 'make_male_mort_haz_inhom', ",
"value 'muM' in 'params' list needs to be a function")
}
# which places have input arcs to this transition
<- trans$s
s
# weights of those arcs
<- trans$s_w
w
# omega is dependent on genotype
<- strsplit(x = u[s], split = "_", fixed = TRUE)[[1]][2]
m_gen <- cube$omega[m_gen]
omega
# return the hazard function
if(exact){
# EXACT hazards (check enabling degree: for discrete simulation only)
return(
function(t,M){
if(w <= M[s]){
return(muM(t) * omega * M[s])
else {
} return(0)
}
}
)
else {
}
# APPROXIMATE hazards (tolerance around zero; for continuous approximation only)
return(
function(t,M){
<- muM(t) * omega * M[s]
haz if(haz < tol){
return(0)
else {
} return(haz)
}
}
)
}# end of function
}
```

Now that we have time-dependent hazard functions, we also need to
replace the hazard generating function, so that our time-dependent
functions are used. The package provided function,
`spn_hazards()`

, generates all of our standard hazard
functions. Below, we replace parts of this function for
time-inhomogeneous hazards: the only differences are for transition
types `"male_mort"`

and `"female_mort"`

, where we
drop in our new functions.

```
<- function(spn_P, spn_T, cube, params,
spn_hazards_lifecycle_node_inhom log_dd = TRUE, exact = TRUE,
tol = 1e-12, verbose = TRUE){
if(tol > 1e-6 & !exact){
warning("warning: hazard function tolerance ",tol," is large; ",
"consider tolerance < 1e-6 for sufficient accuracy\n")
}
if(log_dd){
if(!("K" %in% names(params))){
stop("Specified logistic (carrying capacity) density-dependent larval ",
"mortality, please specify parameter 'K' in params")
}else {
} if(!("gamma" %in% names(params))){
stop("Specified Lotka-Volterra (carrying capacity) density-dependent ",
"larval mortality, please specify parameter 'gamma' in params")
}
}
# transitions and places
<- spn_T$v
v <- spn_P$u
u <- length(v)
n
# get male and larvae indices
<- as.vector(spn_P$ix[[1]]$larvae)
l_ix <- spn_P$ix[[1]]$males
m_ix
# the hazard functions
<- setNames(object = vector("list",n), nm = v)
h
if(verbose){
<- txtProgressBar(min = 1,max = n,style = 3)
pb <- 1
pp
cat(" --- generating hazard functions for SPN --- \n")
}
# make the hazards
for(t in 1:n){
<- spn_T$T[[t]]$class
type
# make the correct type of hazard
if(type == "oviposit"){
<- MGDrivE2:::make_oviposit_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "egg_adv"){
} <- MGDrivE2:::make_egg_adv_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "egg_mort"){
} <- MGDrivE2:::make_egg_mort_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "larvae_adv"){
} <- MGDrivE2:::make_larvae_adv_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact,tol = tol)
else if(type == "larvae_mort"){
} if(log_dd){
<- MGDrivE2:::make_larvae_mort_haz_log(trans = spn_T$T[[t]], u = u,
h[[t]] l_ix = l_ix, node = 1,
cube = cube, params = params,
exact = exact, tol = tol)
else {
} <- MGDrivE2:::make_larvae_mort_haz_lk(trans = spn_T$T[[t]], u = u,
h[[t]] l_ix = l_ix, node = 1,
cube = cube, params = params,
exact = exact, tol = tol)
}else if(type == "pupae_adv"){
} <- MGDrivE2:::make_pupae_adv_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "pupae_mort"){
} <- MGDrivE2:::make_pupae_mort_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "pupae_2m"){
} <- MGDrivE2:::make_pupae_2male_haz(trans = spn_T$T[[t]], u = u,
h[[t]] cube = cube, params = params,
exact = exact, tol = tol)
else if(type == "pupae_2f"){
} <- MGDrivE2:::make_pupae_2female_haz(trans = spn_T$T[[t]], u = u,
h[[t]] m_ix = m_ix, cube = cube,
params = params, exact = exact,
tol = tol)
else if(type == "male_mort"){
} # inhomogeneous male mortality
<- make_male_mort_haz_inhom(trans = spn_T$T[[t]], u = u ,cube = cube,
h[[t]] params = params, exact = exact, tol = tol)
else if(type == "female_mort"){
} # inhomogeneous female mortality
<- make_female_mort_haz_inhom(trans = spn_T$T[[t]], u = u, cube = cube,
h[[t]] params = params, exact = exact, tol = tol)
else if(type == "pupae_2unmated"){
} <- MGDrivE2:::make_pupae_2unmated_haz(trans = spn_T$T[[t]], u = u,
h[[t]] m_ix = m_ix, cube = cube,
params = params, exact = exact,
tol = tol)
else if(type == "female_unmated_mate"){
} <- MGDrivE2:::make_unmated_2female_haz(trans = spn_T$T[[t]], u = u,
h[[t]] m_ix = m_ix, cube = cube,
params = params, exact = exact,
tol = tol)
else if(type == "female_unmated_mort"){
} <- make_female_mort_haz_inhom(trans = spn_T$T[[t]], u = u, cube = cube,
h[[t]] params = params, exact = exact, tol = tol)
else {
} stop(paste0("error in making hazard function for unknown class type: ",type))
}
if(verbose){setTxtProgressBar(pb,t)}
# end loop
}
if(verbose){
close(pb)
cat(" --- done generating hazard functions for SPN --- \n")
}
return(list("hazards"=h, "flag"=exact))
# end function }
```

Much of the following setup is from the “MGDrivE2: One Node Lifecycle Dynamics” vignette, with small adaptations for the time-inhomogeneous simulations.

In a familiar procedure, we define parameters for the simulation. Note that we use the mean of the time-dependent mortality as the equilibrium value, we will use this value to run a set of time-homogeneous simulations to compare results.

We also set the simulation time for 1 year, as our mortality data spans a year. The data is stored daily, and we run a basic Mendelian inheritance pattern.

```
# number of adult female mosquitoes
<- 500
NF
# entomological parameters
<- list(
theta qE = 1/4,
nE = 2,
qL = 1/3,
nL = 3,
qP = 1/6,
nP = 2,
muE = 0.05,
muL = 0.15,
muP = 0.05,
muF = mean(mu_ts[,1]),
muM = mean(mu_ts[,1]),
beta = 16,
nu = 1/(4/24)
)
# simulation parameters
<- 365
tmax <- 1
dt
# basic inheritance pattern
<- MGDrivE::cubeMendelian() cube
```

With parameters defined, we can setup the places and transition in our Petri Net. These structural components will be the same for all of the simulations, time dependent and independent.

```
# Places and transitions for one node
<- spn_P_lifecycle_node(params = theta, cube = cube)
SPN_P <- spn_T_lifecycle_node(spn_P = SPN_P, params = theta, cube = cube)
SPN_T
# Stoichiometry matrix
<- spn_S(spn_P = SPN_P, spn_T = SPN_T) S
```

Since equilibrium is calculated at the average adult mortality over
the year, we use the parameters defined above (`theta`

).
However, the parameters returned by the
`equilibrium_lifeycle()`

function, namely
`initialCons$params$muF`

and `muM`

(adult
mortality rates), need to be replaces with our time-inhomogeneous step function.

```
# calculate equilibrium and setup initial conditions
# outputs required parameters in the named list "params"
# outputs intial equilibrium for adv users, "init
# outputs properly filled initial markings, "M0"
<- equilibrium_lifeycle(params = theta, NF = NF, log_dd = TRUE,
initialCons spn_P = SPN_P, cube = cube)
# store homogenous params
<- initialCons$params
theta_hom
# update params for inhomogeneous hazards (these are function closures)
$params$muF <- step_approx
initialCons$params$muM <- step_approx initialCons
```

Now we are ready to make the vectors of hazards. We need four vectors to compare four different model formulations: time-homogeneous and inhomogeneous, for ODE and CTMC simulations.

```
# ODE (inhomogeneous) hazards
<- spn_hazards_lifecycle_node_inhom(spn_P = SPN_P, spn_T = SPN_T,
approx_hazards cube = cube,
params = initialCons$params,
exact = FALSE, tol = 1e-8,
verbose = FALSE)
# ODE (homogeneous) hazards
<- spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
approx_hazards_hom params = theta_hom, type = "life", log_dd = TRUE,
exact = FALSE, tol = 1e-8, verbose = FALSE)
# CTMC (inhomogeneous) hazards
<- spn_hazards_lifecycle_node_inhom(spn_P = SPN_P, spn_T = SPN_T,
exact_hazards cube = cube,
params = initialCons$params,
exact = TRUE, verbose = FALSE)
# CTMC (homogeneous) hazards
<- spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
exact_hazards_hom params = theta_hom, type = "life", log_dd = TRUE,
exact = TRUE, verbose = FALSE)
```

Finally, before we run simulations, we set up a release scheme to
generate interesting dynamics. We will release 50 adult females and
males, with homozygous recessive alleles, every week for five weeks,
starting at day 25. Remember, it is important that **the event
names match a place name** in the simulation. This format is used
in **MGDrivE2** for consistency with solvers in
`deSolve`

.

```
# releases
<- seq(from = 25, length.out = 5, by = 7)
r_times <- 50
r_size <- data.frame("var" = paste0("F_", cube$releaseType, "_", cube$wildType),
events_f "time" = r_times,
"value" = r_size,
"method" = "add",
stringsAsFactors = FALSE)
<- data.frame("var" = paste0("M_", cube$releaseType),
events_m "time" = r_times,
"value" = r_size,
"method" = "add",
stringsAsFactors = FALSE)
<- rbind(events_f,events_m) events
```

Initially, we should numerically integrate our model, providing exact
results for both time homogeneous and inhomogeneous versions. We
recommend (and use!) the excellent ODE integration routines from
`deSolve`

to solve our deterministic models. Note that we
specify the Runge-Kutta 4/5th order method
(`method = "ode45"`

) in the call to `deSolve::ode`

rather than the default method (`method = "lsoda"`

). This is
because the default solvers behave badly with time-dependent rates. We
clean up the output using functions from the `MGDrivE2`

package and put it in a `data.frame`

for plotting below.

```
# ODE (inhomogeneous) simulation
<- sim_trajectory_R(x0 = initialCons$M0, tmax = tmax, dt = dt,
ODE_out_inhom S = S, hazards = approx_hazards, sampler = "ode",
method = "ode45", events = events, verbose = FALSE)
# summarize
<- summarize_females(out = ODE_out_inhom$state, spn_P = SPN_P)
ODE_female_inhom <- summarize_males(out = ODE_out_inhom$state)
ODE_male_inhom
# combine for plotting later
<- rbind(cbind(ODE_female_inhom, "sex" = "F"),
ODE_out_inhom_melt cbind(ODE_male_inhom, "sex" = "M") )
# ODE (homogeneous) simulation
<- sim_trajectory_R(x0 = initialCons$M0, tmax = tmax, dt = dt,
ODE_out_hom S = S, hazards = approx_hazards_hom, sampler = "ode",
method = "ode45", events = events, verbose = FALSE)
# summarize
<- summarize_females(out = ODE_out_hom$state, spn_P = SPN_P)
ODE_female_hom <- summarize_males(out = ODE_out_hom$state)
ODE_male_hom
# combine for plotting later
<- rbind(
ODE_out_hom_melt cbind(ODE_female_hom, "sex" = "F"),
cbind(ODE_male_hom, "sex" = "M")
)
$death <- "inhomogeneous"
ODE_out_inhom_melt$death <- "homogeneous"
ODE_out_hom_melt
# plot everything together
ggplot(data = rbind(ODE_out_inhom_melt, ODE_out_hom_melt) ) +
geom_path(aes(x = time, y = value,
color = death)) +
facet_grid(genotype ~ sex, scales = "free_y") +
scale_color_manual(values = c("inhomogeneous" = "firebrick3",
"homogeneous" = "dodgerblue3")) +
# final formatting
theme_bw() +
ggtitle("Constant vs Seasonally Time-Varying Mortality Rates") +
guides(color = FALSE)
#> Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
#> of ggplot2 3.3.4.
```

We run 10 realizations of the stochastic simulations, to get a better
sense of the overall variability in trajectories. We can run these
repetitions in two ways: using the built-in repetitions provided by
**MGDrivE2**, or running a loop over
`sim_trajectory_R()`

to generate repetitions. Both of these
are valuable, but in different circumstances. The built-in repetitions
provided by **MGDrivE2** are deliberately single-threaded
(as are all analysis routines), and more efficient than running single
repetitions. This is useful for parameter sweeps, where several
repetitions of each parameter needs to be run. However, for very large
repetitions, it is not practical to run all of the repetitions on a
single-thread. In this instance, and when running many parameter
combinations, it is better to setup a parallel cluster, and evaluate
multiple trajectories at once. For demonstration purposes, we will setup
both methods.

We need a few extra parameters for the stochastic realizations, namely the number of realizations and the time-step. We will run 10 repetitions, using a time step equal to one hour.

First we simulate the time-inhomogeneous Markov model, using the built-in repetition wrapper. Please note that this vignette does not run the following simulation code to combat excessive build times; however the code can be run locally after building the Petri net and hazards as done previously in this vignette.

```
# parameters for stochastic simulations
<- 10
num_rep <- 1/24
dt_stoch
# run inhomogeneous simulations
<- sim_trajectory_R(x0 = initialCons$M0, tmax = tmax, dt = dt,
PTS_out_inhom dt_stoch = dt_stoch, S = S, num_reps = num_rep,
hazards = exact_hazards, sampler = "tau",
events = events, verbose = FALSE)
# summarize females/males, add sex for plotting later
<- cbind(summarize_females(out = PTS_out_inhom$state, spn_P = SPN_P),
PTS_female "sex" = "F")
<- cbind(summarize_males(out = PTS_out_inhom$state), "sex" = "M")
PTS_male
# reductions for plotting
<- rbind(PTS_female, PTS_male)
PTS_inhom_par_unroll
# summarize_* functions return long-form data for ggplot2
# therefore, we take the first rep of data, the sim_time by num_genotypes
# Then, we reorganize the data into an array, and take the mean over every
# repetition
<- tmax + 1
tot_time <- cube$genotypesN
num_geno <- rbind(
PTS_inhom_par_unroll_mean cbind(PTS_female[1:(tot_time * num_geno), c("genotype", "sex", "time")],
"Mean" = as.vector(rowMeans(x = array(data = PTS_female$value,
dim = c(tot_time, num_geno, num_rep)),
dims = 2)) ),
cbind(PTS_male[1:(tot_time * num_geno), c("genotype", "sex", "time")],
"Mean" = as.vector(rowMeans(x = array(data = PTS_male$value,
dim = c(tot_time, num_geno, num_rep)),
dims = 2)) )
)
```

For the time-homogeneous simulation, we will demonstrate how to simulate many trajectories in parallel.

There are many packages available for parallel evaluation in
`R`

. The default package within `R`

is the
`parallel`

package. Other useful packages include foreach, doSNOW, and future. Because
`parallel`

is a base package, we use it to setup a socket
cluster (this cluster works on Windows and nix systems) for this
example. We then use `parallel::clusterSetRNGStream`

to set
independent, reproducible streams of random numbers for each core in the
cluster.

```
# parameters for stochastic simulations
<- 1
num_core <- initialCons$M0
M0
# setup the cluster
<- parallel::makePSOCKcluster(names = num_core)
cl
# set parallel seed
::clusterSetRNGStream(cl = cl, iseed = 18438L)
parallel
# export required objects to each socket
::clusterExport(cl=cl, varlist=c("M0", "tmax", "dt", "dt_stoch", "S",
parallel"exact_hazards_hom", "events", "SPN_P"))
# load MGDrivE2 on each socket
::clusterEvalQ(cl=cl, expr={library(MGDrivE2)}) parallel
```

Finally, we evaluate our function on each cluster using the
load-balanced apply statement, `parallel::clusterApplyLB()`

.
This will run each repetition, analyze it, and store it in a list for
further analysis. In this example, load-balancing is not particularly
important, as these simulations are short and small, thus will have
fairly consistent run times. Especially for parameter sweeps, some
combinations will create significantly more complicated, and thus
longer-running, dynamics, making load-balancing an important
consideration.

```
# run homogeneous simulations
<- parallel::clusterApplyLB(cl=cl, x=1:num_rep, fun=function(x){
PTS_hom_par # run trajectory
<- sim_trajectory_R(x0 = M0, tmax = tmax, dt = dt,
PTS_out_hom dt_stoch = dt_stoch, S = S, hazards = exact_hazards_hom,
sampler = "tau",events = events, verbose = FALSE)
# summarize females/males for plotting later
<- cbind(summarize_females(out = PTS_out_hom$state, spn_P = SPN_P),
f_sum "rep"=x, "sex"="F")
<- cbind(summarize_males(out = PTS_out_hom$state), "rep"=x, "sex"="M")
m_sum
# return single dataframe
return(rbind(f_sum, m_sum))
})
# stop cluster
::stopCluster(cl)
parallel
# summarize data
# the Reduce() call generates a mean of the populations
<- do.call(what = rbind, args = PTS_hom_par)
PTS_hom_par_unroll <- cbind(PTS_hom_par[[1]][ ,c("genotype", "sex", "time")],
PTS_hom_par_unroll_mean "Mean" = Reduce(f = "+", x = lapply(X = PTS_hom_par,
FUN = function(x){x[,3]})
/num_rep) )
```

Finally we combine the data into one plot. The ODE trajectories are plotted as dashed lines, the mean of the stochastic simulations as solid lines, and each individual stochastic trajectory is plotted as a faint curve. Lines in red are the time-inhomogeneous model runs and blue are the time-homogeneous simulations.

```
# add mortality for plotting
$death <- "inhomogeneous"
PTS_inhom_par_unroll$death <- "homogeneous"
PTS_hom_par_unroll$death <- "inhomogeneous"
ODE_out_inhom_melt$death <- "homogeneous"
ODE_out_hom_melt
# plot everything together
ggplot(data = rbind(PTS_inhom_par_unroll, PTS_hom_par_unroll) ) +
geom_path(aes(x = time, y = value, group = interaction(rep, death),
color = death), alpha = 0.15) +
facet_grid(genotype ~ sex, scales = "free_y") +
scale_color_manual(values = c("inhomogeneous" = "firebrick3",
"homogeneous" = "dodgerblue3")) +
# inhomogeneous mean
geom_line(data = PTS_inhom_par_unroll_mean, mapping = aes(x = time, y = Mean),
color = "firebrick4") +
# homogeneous mean
geom_line(data = PTS_hom_par_unroll_mean, mapping = aes(x = time, y = Mean),
color = "dodgerblue4") +
# ODE inhomogeneous
geom_line(data = ODE_out_inhom_melt, mapping = aes(x = time, y = value),
linetype = 2, color = "firebrick4") +
# ODE homogeneous
geom_line(data = ODE_out_hom_melt, mapping = aes(x = time, y = value),
linetype = 2, color = "dodgerblue4") +
# final formatting
theme_bw() +
ggtitle("Constant vs Seasonally Time-Varying Mortality Rates") +
guides(color = FALSE)
```

There is a clear difference in population size over the course of a year. Since we used the average mortality rate to calculate the equilibrium, we see that the time-inhomogeneous simulations center around our time-homogeneous ones. Comparing the dashed-lines (ODE trajectories) to the solid-lines (SPN averages), we see that our stochastic realizations closely follow their deterministic solutions. However, the faint lines indicate significant variation is possible.

- Anderson, David F. “A modified next reaction method for simulating chemical systems with time dependent propensities and delays.” The Journal of chemical physics 127.21 (2007): 214107.
- Thanh, Vo Hong, and Corrado Priami. “Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm.” The Journal of chemical physics 143.5 (2015): 08B601_1.
- Allen, Linda JS. “A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis.” Infectious Disease Modelling 2.2 (2017): 128-142.