# Simple behavioral state mosquito model

library(MicroMoB)
library(ggplot2)
library(data.table)
library(parallel)

The simple behavioral state mosquito model has two behavioral states which mosquitoes can exist in: blood feeding ($$B$$) and oviposition ($$Q$$). When mosquitoes are in $$B$$ they will attempt to blood feed until they are successful, at which point they transition to $$Q$$ and attempt to oviposit an egg batch. Upon emergence, mosquitoes are primed for blood feeding and are in $$B$$. They transition between these states until they die, which occurs according to the state dependent probabilities $$p_{B}$$ and $$p_{Q}$$ (these may also vary by location and time). The model does not consider male mosquitoes.

The model also considers infection. Uninfected (susceptible) mosquitoes $$M$$ may become infected if they are in $$B$$, successfully take a blood meal, and are infected (with probability $$\kappa$$). They then transition to the infected class $$Y$$, in behavioral state $$Q$$. The extrinsic incubation period (EIP) may vary with time, and they advance until they become infectious (if they survive), where they remain until death. Both dynamics operate simultaneously.

## Deterministic model

The deterministic behavioral state model has the following form:

$$$\left[ \begin{array}{cc} B_{t+1} \\ Q_{t+1} \\ \end{array} \right] = \left[ \begin{array}{ccc} (1 - \psi_b) \Psi_{b b} & \psi_q \Psi_{q b} \\ \psi_b \Psi_{b q} & (1 - \psi_q) \Psi_{q q} \\ \end{array} \right] \left[ \begin{array}{cc} p_b B_{t} \\ p_q Q_{t} \\ \end{array} \right] + \left[ \begin{array}{c} \Lambda_{t} \\ 0 \\ \end{array} \right]$$$

The state is a column vector $$\left[\begin{array}{cc} B \\ Q \\ \end{array}\right]$$. We assume that there are $$p$$ locations where mosquitoes go to seek blood hosts, so that the first $$p$$ elements correspond to the number of mosquitoes in the $$B$$ state at those places. There are $$l$$ locations where mosquitoes go to oviposit (aquatic habitats), so the last $$l$$ elements in the vector are mosquitoes in the $$Q$$ state. There is no requirement that the set of points where mosquitoes blood feed and oviposit be distinct, although they may be.

The infection states are similar to the Ross-Macdonald model, see vignette("RM_mosquito") for more details.

The parameters in the state updating equation are:

• $$\psi_b$$: probability of successful blood feeding (vector of length $$p$$); this parameter is computed from $$f, q$$ (themselves calculated during the bloodmeal algorithm) as $$1-e^{-fq}$$.
• $$\psi_q$$: probability of successful oviposition (vector of length $$l$$).
• $$\Psi_{b b}$$: transition probability matrix for movement among blood feeding haunts. It has dimension $$p\times p$$, and has columns that sum to 1 (note state vectors are on the right).
• $$\Psi_{q b}$$: transition probability matrix for movement from aquatic habitats to blood feeding haunts. It has dimension $$p\times l$$.
• $$\Psi_{b q}$$: transition probability matrix for movement from blood feeding haunts to aquatic habitats. It has dimension $$l\times p$$.
• $$\Psi_{q q}$$: transition probability matrix for movement among aquatic habitats. It has dimension $$l\times l$$.
• $$p_{B}$$: daily survival probability for blood feeding mosquitoes.
• $$p_{Q}$$: daily survival probability for ovipositing mosquitoes.

## Stochastic model

The stochastic model has similar updating dynamics to the deterministic implementation, except that all survival and success probabilities are used in binomial draws and movement is drawn from a multinomial distribution.

## Simulation

We assume that $$p = l = 1$$ and that the total mosquito density $$M = B + Q$$ is known, and that we want to solve for the emergence rate $$\Lambda$$ such that the system is at equilibrium. Rewriting the equations when we substitute $$Q = M - B$$ and $$B = M - Q$$ we solve the state variables as:

$$$Q = \frac{Mp_{B}\Psi_{B}}{p_{B}\Psi_{B} - p_{Q}(1-\Psi_{Q}) + 1} \\ B = \frac{M-Mp_{Q}(1-\Psi_{Q})}{p_{B}\Psi_{B} - p_{Q}(1-\Psi_{Q}) + 1}$$$

Then the first equation can simply be rearranged to yield:

$$$\Lambda = B - p_{B}(1-\Psi_{B})B - p_{Q}\Psi_{Q}Q$$$

And now the model with 1 point of each type can be set up at equilibrium. We will use the Beverton-Holt model of aquatic ecology demonstrated in vignette("BH_aqua"), which will be parameterized to provide the correct equilibrium $$\Lambda$$.

p <- l <- 1
tmax <- 1e2

M <- 120
pB <- 0.8
pQ <- 0.95
PsiB <- 0.5
PsiQ <- 0.85

B <- (M - (M*pQ*(1-PsiQ))) / ((pB*PsiB) - (pQ*(1-PsiQ)) + 1)
Q <- (M*pB*PsiB) / ((pB*PsiB) - (pQ*(1-PsiQ)) + 1)

lambda <- B - (pB*(1-PsiB)*B) - (pQ*PsiQ*Q)

nu <- 25
eggs <- nu * PsiQ * Q

# static pars
molt <-  0.1
surv <- 0.9

# solve L
L <- lambda * ((1/molt) - 1) + eggs
K <- - (lambda * L) / (lambda - L*molt*surv)

Let’s set up the model. We use make_MicroMoB() to set up the base model object, and setup_aqua_BH() for the Beverton-Holt aquatic model with our chosen parameters. setup_mosquito_BQ() will set up a behavioral state model of adult mosquito dynamics.

We run a deterministic simulation and store output in a matrix. Note that we calculate the f and q parameters to achieve the correct PsiB probability; normally these would be updated dynamically during the bloodmeal but we are running a mosquito-only simulation so we set these deterministically.

# deterministic run
mod <- make_MicroMoB(tmax = tmax, p = p, l = l)
setup_aqua_BH(model = mod, stochastic = FALSE, molt = molt, surv = surv, K = K, L = L)
setup_mosquito_BQ(model = mod, stochastic = FALSE, eip = 5, pB = pB, pQ = pQ, psiQ = PsiQ, Psi_bb = matrix(1), Psi_bq = matrix(1), Psi_qb = matrix(1), Psi_qq = matrix(1), nu = nu, M = c(B, Q), Y = matrix(0, nrow = 2, ncol = 6))

out_det <- data.table::CJ(day = 1:tmax, state = c('L', 'A', 'B', 'Q'), value = NaN)
out_det <- out_det[c('L', 'A', 'B', 'Q'), on="state"]
data.table::setkey(out_det, day)

mod$mosquito$q <- 0.3
mod$mosquito$f <- log(1 - PsiB) / -0.3

while (get_tnow(mod) <= tmax) {

step_aqua(model = mod)
step_mosquitoes(model = mod)

out_det[day == get_tnow(mod) & state == 'L', value := mod$aqua$L]
out_det[day == get_tnow(mod) & state == 'A', value := mod$aqua$A]
out_det[day == get_tnow(mod) & state == 'B', value := mod$mosquito$M[1]]
out_det[day == get_tnow(mod) & state == 'Q', value := mod$mosquito$M[2]]

mod$global$tnow <- mod$global$tnow + 1L
}

Now we run the same model, but using the option stochastic = TRUE for our dynamics, and draw 10 trajectories.

# stochastic runs
out_sto <- mclapply(X = 1:10, FUN = function(runid) {

mod <- make_MicroMoB(tmax = tmax, p = p, l = l)
setup_aqua_BH(model = mod, stochastic = TRUE, molt = molt, surv = surv, K = K, L = L)
setup_mosquito_BQ(model = mod, stochastic = TRUE, eip = 5, pB = pB, pQ = pQ, psiQ = PsiQ, Psi_bb = matrix(1), Psi_bq = matrix(1), Psi_qb = matrix(1), Psi_qq = matrix(1), nu = nu, M = c(B, Q), Y = matrix(0, nrow = 2, ncol = 6))

out <- data.table::CJ(day = 1:tmax, state = c('L', 'A', 'B', 'Q'), value = NaN)
out <- out[c('L', 'A', 'B', 'Q'), on="state"]
data.table::setkey(out, day)

mod$mosquito$q <- 0.3
mod$mosquito$f <- log(1 - PsiB) / -0.3

while (get_tnow(mod) <= tmax) {
step_aqua(model = mod)
step_mosquitoes(model = mod)

out[day == get_tnow(mod) & state == 'L', value := mod$aqua$L]
out[day == get_tnow(mod) & state == 'A', value := mod$aqua$A]
out[day == get_tnow(mod) & state == 'B', value := mod$mosquito$M[1]]
out[day == get_tnow(mod) & state == 'Q', value := mod$mosquito$M[2]]

mod$global$tnow <- mod$global$tnow + 1L
}

out[, 'run' := as.integer(runid)]
return(out)
})

Now we process the output and plot the results. Deterministic solutions are solid lines and each stochastic trajectory is a faint line.

out_sto <- data.table::rbindlist(out_sto)

ggplot(data = out_sto) +
geom_line(aes(x = day, y = value, color = state, group = run), alpha = 0.35) +
geom_line(data = out_det, aes(x = day, y = value, color = state)) +
facet_wrap(. ~ state, scales = "free")