Preface

This vignette describes the SEIR (Susceptible-Exposed-Infectious-Recovered) human model of epidemiological dynamics. It is intended that readers are already familiar with the content in the vignettes “MGDrivE2: One Node Epidemiological Dynamics” and “MGDrivE2: Metapopulation Network Epidemiological Dynamics”, as this vignette primarily describes the coupling of the SEIR human model to the system. The SEIR model is appropriate for strongly immunizing diseases (such as Yellow fever or Japanese Encephalitis) which also have a latent period (E). The E state represents the time needed for the pathogen to colonize and replicate within the host before the host becomes infectious to mosquitoes. This time where an individual is infected but not infected is called the latent period, or sometimes, the exposed period (Martcheva 2015). Because this vignette assumes familiarity with the other vignettes, we will be brief about any parts of setting up the simulation not specific to the SEIR human model.

We start by loading the MGDrivE2 package, as well as the MGDrivE package for access to inheritance cubes, ggplot2 for graphical analysis, and Matrix for sparse matrices used in migration. We will use the basic cube to simulate Mendelian inheritance for this example.

# simulation functions
library(MGDrivE2)
# inheritance patterns
library(MGDrivE)
# plotting
library(ggplot2)
# sparse migration
library(Matrix)

# basic inheritance pattern
cube <- MGDrivE::cubeMendelian()

Parameterization

The majority of parameters are concerned with the genetic and mosquito lifecycle model, as used in the SEI-SIS model, so the setup of the SIS-SEIR model is nearly identical, with the addition of a single parameter, delta, the inverse duration of the latent state (the dwell time in E). We consider a situation where an equilibrium population of 1000 female mosquitoes exists. For definition of the epidemiological parameters, please see the vignette “MGDrivE2: One Node Epidemiological Dynamics” and “MGDrivE2: One Node Lifecycle Dynamics” for the entomological (lifecycle) parameters.

Additionally, we set the simulation time to 300 days and store output from each day.

# entomological and epidemiological parameters
theta <- list(
  # lifecycle parameters
  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 = 0.09,
  muM = 0.09,
  beta = 16,
  nu = 1/(4/24),
  # epidemiological parameters
  NH = 250,
  X = c(1,0,0,0), # 0% disease incidence
  NFX = 1000,
  f = 1/3,
  Q = 0.9,
  b = 0.55,
  c = 0.15,
  delta = 1/5,
  r = 1/14,
  muH = 1/(62*365),
  qEIP = 1/11,
  nEIP = 3
)

# simulation parameters
tmax <- 250
dt <- 1

Next, we need to augment the cube with genotype specific transmission efficiencies; b and c, the mosquito to human and human to mosquito transmission efficiencies. We assume that transmission from human to mosquito is not impacted in modified mosquitoes, but mosquito to human transmission is significantly reduced in modified mosquitoes. For detailed descriptions of these parameters for modelling malaria transmission, see Smith & McKenzie (2004) for extensive discussion.

# augment the cube with RM transmission parameters
cube$c <- setNames(object = rep(x = theta$c, times = cube$genotypesN), nm = cube$genotypesID)
cube$b <- c("AA" = theta$b, "Aa" = 0.35, "aa" = 0)

Initialization of the Petri Net

Just like the SEI-SIS disease transmission model, the SIS-SEIR sits “on top” of the existing MGDrivE2 structure. The only difference between these two models are the human dynamics. See the “MGDrivE2: One Node Epidemiological Dynamics” vignette for more details about how this is done.

With the parameters defined, we use SIS-SEIR model specific functions to setup the places and transitions in the Petri Net.

# Places and transitions
SPN_P <- spn_P_epiSEIR_node(params = theta, cube = cube)
SPN_T <- spn_T_epiSEIR_node(spn_P = SPN_P, params = theta, cube = cube)

# Stoichiometry matrix
S <- spn_S(spn_P = SPN_P, spn_T = SPN_T)

Equilibrium Conditions and Hazard Functions

Once the initial markings of the Petri Net are setup, we can combine them with the parameters defined above to calculate the population distribution, at equilibrium, and the initial conditions.

It should be noted that for realistic parameter values, this model only has a non-trivial equilibrium when $\mu_{H} < \frac{\mu_{H} \cdot N_{H} - r \cdot I_{H}}{I_{H}}$, which results in extremely unrealistic (short) lifespans. The other two equilibrium points are the trivial disease free equilibrium when λ_H = 0 and the normal (λ_H > 0) case when R_H(∞) → N_H, that is, for realistic values of parameters, all surviving individuals will become infected. For that reason we do not explicitly calculate the endemic equilibrium (where $\lambda_{H} = a \cdot b \cdot \frac{1}{N_{H}} \cdot I_{V}$ is the force of infection on humans). In general the SEIR human model should be used to evaluate the impact of gene drive interventions on one-off epidemic situations (e.g.; does releasing large numbers of modified mosquitoes 10 days after initial cases appear make a significant difference in final outcome), rather than for investigating endemic diseases, which require more complex models with waning immunity.

The function equilibrium_SEI_SEIR() calculates the equilibrium distribution of female mosquitoes across SEI stages, based on human populations and force-of-infection, then calculates all other equilibria. We set the logistic form for larval density-dependence in these examples by specify log_dd = TRUE.

# SEI mosquitoes and SEIR humans equilibrium
#  outputs required parameters in the named list "params"
#  outputs initial equilibrium for adv users, "init
#  outputs properly filled initial markings, "M0"
initialCons <- equilibrium_SEI_SEIR(params = theta, log_dd = TRUE, spn_P = SPN_P,
                                    cube = cube)

Finally, we make the hazard functions for the ODE approximation and the discrete stochastic model.

# approximate hazards for continous approximation
approx_hazards <- spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
                              params = initialCons$params, type = "SEIR",
                              log_dd = TRUE, exact = FALSE, tol = 1e-8,
                              verbose = FALSE)

# exact hazards for integer-valued state space
exact_hazards <- spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
                             params = initialCons$params, type = "SEIR",
                             log_dd = TRUE, exact = TRUE, verbose = FALSE)

Simulation of Fully Specified SPN Model

In order to simulate an epidemic, we will introduce 100 exposed humans at day 5. We will assume that by day 25 the decision is made to release modified mosquitoes. Five releases are preformed every other day. Remember, it is critically important that the event names match a place name in the simulation. The simulation function checks this and will throw an error if the event name does not exist as a place in the simulation. This format is used in MGDrivE2 for consistency with solvers in deSolve.

# releases
r_times <- seq(from = 25, length.out = 5, by = 2)
r_size <- 50
m_events <- data.frame("var" = paste0("F_", cube$releaseType, "_", cube$wildType, "_S"),
                       "time" = r_times,
                       "value" = r_size,
                       "method" = "add",
                       stringsAsFactors = FALSE)

h_events <- data.frame("var" = "H_E",
                       "time" = 5,
                       "value" = 100,
                       "method" = "add",
                       stringsAsFactors = FALSE)

events <- rbind(m_events, h_events)

# run deterministic simulation
ODE_out <- sim_trajectory_R(x0 = initialCons$M0, t0 = 0, tt = tmax, dt = dt, S = S,
                            hazards = approx_hazards, sampler = "ode", method = "lsoda",
                            events = events, verbose = FALSE)

# summarize females/humans by genotype
ODE_female <- summarize_females_epi(out = ODE_out$state,spn_P = SPN_P)
ODE_humans <- summarize_humans_epiSEIR(out = ODE_out$state)

# add species for plotting
ODE_female$species <- "Mosquitoes"
ODE_humans$species <- "Humans"

# plot
ggplot(data = rbind(ODE_female, ODE_humans) ) +
  geom_line(aes(x = time, y = value, color = genotype, linetype = inf)) +
  facet_wrap(. ~ species, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: ODE solution")

# delta t
dt_stoch <- 0.2

# run tau-leaping simulation
PTS_out <- sim_trajectory_R(x0 = initialCons$M0, t0 = 0, tt = tmax, dt = dt,
                            dt_stoch = dt_stoch, S = S, hazards = exact_hazards,
                            sampler = "tau", events = events, verbose = FALSE)

# summarize females/humans by genotype
PTS_female <- summarize_females_epi(out = PTS_out$state,spn_P = SPN_P)
PTS_humans <- summarize_humans_epiSEIR(out = PTS_out$state)

# add species for plotting
PTS_female$species <- "Mosquitoes"
PTS_humans$species <- "Humans"

# plot
ggplot(data = rbind(PTS_female, PTS_humans) ) +
  geom_line(aes(x = time, y = value, color = genotype, linetype = inf)) +
  facet_wrap(. ~ species, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: Tau-leaping Approximation")

Parameterization

To setup a three-node metapopulation network, with 1000 females in the mosquito-only node, 500 adult female mosquitoes and 250 humans in the shared location, and 250 humans in the human-only node, we first update the parameter object (theta) to reflect the new populations. We then set the simulation time to 300 days, but store output every other day this time.

We will be using the same inheritance cube for these simulations, so none of the parameters for that need updated.

# 3-population entomological and epidemiological parameters
theta <- list(
  # lifecycle parameters
  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 = 0.09,
  muM = 0.09,
  beta = 16,
  # epidemiological parameters
  NH = 250,
  X = c(1,0,0,0),
  NFX = 500, # needed if any X[ ,3] == 0
  f = 1/3,
  Q = 0.9,
  b = 0.55,
  c = 0.15,
  delta = 1/5,
  nu = 1/5,
  r = 1/14,
  muH = 1/(62*365),
  qEIP = 1/11,
  nEIP = 3
)

# simulation parameters
tmax <- 250
dt <- 2

Initialization of the Petri Net

Next, we create the markings for a three-node Petri Net. We specify edges in the movement network separately for humans and mosquitoes (in h_move and m_move, respectively). Humans can move back and forth between the humans-only and both nodes, and mosquitoes between the mosquitoes-only and both nodes. These matrices are needed to generate the set of transitions. For more information, see the “MGDrivE2: Metapopulation Network Epidemiological Dynamics” vignette. Once the movement is specified, we can setup the places and transitions for the Petri Net.

# nodetypes
node_list <- c("h", "b", "m")
num_nodes <- length(node_list)

# human movement
h_move <- matrix(data = FALSE, nrow = num_nodes, ncol = num_nodes,
                 dimnames = list(node_list, node_list))
h_move[1,2] <- TRUE
h_move[2,1] <- TRUE

# mosquito movement
m_move <- matrix(data = FALSE, nrow = num_nodes, ncol = num_nodes,
                 dimnames = list(node_list, node_list))
m_move[2,3] <- TRUE
m_move[3,2] <- TRUE

# Places and transitions
SPN_P <- spn_P_epiSEIR_network(node_list = node_list, params = theta, cube = cube)
SPN_T <- spn_T_epiSEIR_network(node_list = node_list, spn_P = SPN_P, params = theta,
                               cube = cube, h_move = h_move, m_move = m_move)

# Stoichiometry matrix
S <- spn_S(spn_P = SPN_P, spn_T = SPN_T)

Equilibrium Conditions and Hazard Functions

Now that we have set up the structural properties of the Petri Net, we can calculate the population equilibrium, and the initial conditions, from parameters defined earlier ( theta ). Remember, these are node-by-node equilibria, not an equilibrium over the entire network.

# SEI mosquitoes and SEIR humans equilibrium
#  outputs required parameters in the named list "params"
#  outputs initial equilibrium for adv users, "init
#  outputs properly filled initial markings, "M0"
initialCons <- equilibrium_SEI_SEIR(params = theta,node_list = node_list,
                                    NF = 1000,
                                    phi=0.5,
                                    NH = 250,
                                    log_dd=TRUE,
                                    spn_P=SPN_P,
                                    pop_ratio_Aq=NULL, pop_ratio_F=NULL,
                                    pop_ratio_M=NULL, cube=cube)

We’ll make the movement matrices using the same parameters as “MGDrivE2: Metapopulation Network Epidemiological Dynamics”. Reminder, for both humans and mosquitoes, the vector of movement rates must be of length equal to the number of nodes in the matrix, and nodes from which no movement is possible (for mosquitoes, human-only nodes and vice versa for humans), should have value NaN. If you have made the logical matrices specifying allowed movement, the following code can help ascertain which elements should be NaN: apply(X = m_move, MARGIN = 1, FUN = Negate(any)). Finally we append the movement objects to the vector of parameters initialCons$params.

# calculate movement rates and movement probabilities
gam <- calc_move_rate(mu = initialCons$params$muF, P = 0.05)

# set mosquito movement rates/probabilities
#  mosquitoes exist in nodes 2 and 3, not 1
mr_mosy <- c(NaN, gam, gam)
mp_mosy <- Matrix::sparseMatrix(i = c(2,3), j = c(3,2), x = 1, dims = dim(m_move))

# set human movement rates/probabilities
#  humans exist in nodes 1 and 2, not 3
mr_human <- c(1/7, 1/7, NaN)
mp_human <- Matrix::sparseMatrix(i = c(1,2), j = c(2,1), x = 1, dims = dim(h_move))

# put rates and probs into the parameter list
initialCons$params$mosquito_move_rates <- mr_mosy
initialCons$params$mosquito_move_probs <- mp_mosy
initialCons$params$human_move_rates <- mr_human
initialCons$params$human_move_probs <- mp_human

Now that all the necessary parameters have been added to the named list initialCons$params, we generate the hazard functions, using the function spn_hazards(). By specifying log_dd = TRUE, we use logistic density dependence for these simulations.

# approximate hazards for continous approximation
approx_hazards <- spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
                              params = initialCons$params , type = "SEIR",
                              log_dd = TRUE, exact = FALSE, tol = 1e-8,
                              verbose = FALSE)

Simulation of Fully Specified SPN Model

In order to simulate an epidemic, we will introduce 100 exposed humans at day 5 into node 1. We will assume that by day 25 the decision is made to release modified mosquitoes, but into node 3 only. Five releases are preformed every other day. This will allow us to see migration between human nodes, and mosquito nodes, before seeing interactions that cause disease dynamics. Remember, it is critically important that the event names match a place name in the simulation. The simulation function checks this and will throw an error if the event name does not exist as a place in the simulation. This format is used in MGDrivE2 for consistency with solvers in deSolve.

# releases
r_times <- seq(from = 25, length.out = 5, by = 2)
r_size <- 50
m_events <- data.frame("var" = paste0("F_", cube$releaseType, "_", cube$wildType, "_S_3"),
                       "time" = r_times,
                       "value" = r_size,
                       "method" = "add",
                       stringsAsFactors = FALSE)

h_events <- data.frame("var" = "H_E_1",
                       "time" = 5,
                       "value" = 100,
                       "method" = "add",
                       "stringsAsFactors" = FALSE)

events <- rbind(m_events,  h_events)

# run deterministic simulation
ODE_out <- sim_trajectory_R(x0 = initialCons$M0, t0 = 0, tt = tmax, dt = dt, S = S,
                            hazards = approx_hazards, sampler = "ode", method = "lsoda",
                            events = events, verbose = FALSE)
#> Warning in base_events(x0 = x0, events = events, dt = dt): event times do not correspond to a multiple of dt.
#> event times will be rounded up to the nearest time-step!

# summarize aquatic stages by genotype
ODE_e <- summarize_eggs_geno(out = ODE_out$state, spn_P = SPN_P)
ODE_l <- summarize_larvae_geno(out = ODE_out$state, spn_P = SPN_P)
ODE_p <- summarize_pupae_geno(out = ODE_out$state, spn_P = SPN_P)

# add stage name
ODE_e$stage <- "Egg"
ODE_l$stage <- "Larvae"
ODE_p$stage <- "Pupae"

# plot by genotype
ggplot(data = rbind(ODE_e, ODE_l,ODE_p)) +
  geom_line(aes(x = time, y = value, color = genotype)) +
  facet_grid(stage ~ node, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: ODE Solution - Genotypes")

# summarize aquatic stages by Erlang stage
ODE_e <- summarize_eggs_stage(out = ODE_out$state, spn_P = SPN_P)
ODE_l <- summarize_larvae_stage(out = ODE_out$state, spn_P = SPN_P)
ODE_p <- summarize_pupae_stage(out = ODE_out$state, spn_P = SPN_P)

# add stage name
ODE_e$stage <- "Egg"
ODE_l$stage <- "Larvae"
ODE_p$stage <- "Pupae"

# plot by Erlang stage
ggplot(data = rbind(ODE_e, ODE_l,ODE_p)) +
  geom_line(aes(x = time, y = value, color = `Erlang-stage`)) +
  facet_grid(stage ~ node, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: ODE Solution - Erlang Dwell Stage")

# summarize females/males
ODE_f <- summarize_females_epi(out = ODE_out$state, spn_P = SPN_P)
ODE_m <- summarize_males(out = ODE_out$state)

# add sex for plotting
ODE_f$sex <- "Female"
ODE_m$sex <- "Male"
ODE_m$inf <- "S"

# plot adults
ggplot(data = rbind(ODE_f, ODE_m)) +
  geom_line(aes(x = time, y = value, color = genotype, linetype = inf)) +
  facet_grid(sex ~ node, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: ODE Solution - Adult Stages")

# summarize females/humans by genotype
ODE_female <- summarize_females_epi(out = ODE_out$state, spn_P = SPN_P)
ODE_humans <- summarize_humans_epiSEIR(out = ODE_out$state)

# plot
ggplot(data = rbind(ODE_female,ODE_humans) ) +
  geom_line(aes(x = time, y = value, color = genotype, linetype = inf)) +
  facet_wrap(. ~ node, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: ODE Solution")

# delta t
dt_stoch <- 0.1

# run cle simulation
CLE_out <- sim_trajectory_R(x0 = initialCons$M0, t0 = 0, tt = tmax, dt = dt,
                            dt_stoch = dt_stoch, S = S, hazards = approx_hazards,
                            sampler = "cle", events = events, verbose = FALSE)
#> Warning in base_events(x0 = x0, events = events, dt = dt): event times do not correspond to a multiple of dt.
#> event times will be rounded up to the nearest time-step!

# summarize females/humans by genotype
CLE_female <- summarize_females_epi(out = CLE_out$state, spn_P = SPN_P)
CLE_humans <- summarize_humans_epiSEIR(out = CLE_out$state)

# plot
ggplot(data = rbind(CLE_female,CLE_humans) ) +
  geom_line(aes(x = time, y = value, color = genotype, linetype = inf)) +
  facet_wrap(. ~ node, scales = "free_y") +
  theme_bw() +
  ggtitle("SPN: CLE Approximation")