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Hybrid MoI (Multiplicity of Infection) Human Model

Source: vignettes/human_hmoi.Rmd
human_hmoi.Rmd

This is a hybrid model which tracks the mean multiplicity of infection (superinfection) in two compartments. The first, \(m1\) is all infections, and the second \(m2\) are apparent (patent) infections. Therefore \(m2\) is “nested” within \(m1\). It is a “hybrid” model in the sense of Nåsell (1985).

Differential Equations

The equations are as follows:

\[ \dot{m_{1}} = h - r_{1}m_{1} \] \[ \dot{m_{2}} = h - r_{2}m_{2} \] Where \(h = b EIR\), is the force of infection. Prevalence can be calculated from these MoI values by:

\[ x_{1} = 1-e^{-m_{1}} \] \[ x_{2} = 1-e^{-m_{2}} \] The net infectious probability to mosquitoes is therefore given by:

\[ x = c_{2}x_{2} + c_{1}(x_{1}-x_{2}) \]

Where \(c_{1}\) is the infectiousness of inapparent infections, and \(c_{2}\) is the infectiousness of patent infections.

Equilibrium solutions

One way to proceed is assume that \(m_{2}\) is known, as it models the MoI of patent (observable) infections. Then we have:

\[ h = r_{2}/m_{2} \] \[ m_{1} = h/r_{1} \] We can use this to calculate the net infectious probability, and then \(\kappa = \beta^{\top} \cdot x\), allowing the equilibrium solutions of this model to feed into the other components.

Example

Here we run a simple example with 3 population strata at equilibrium. We use exDE::make_parameters_X_hMoI to set up parameters. Please note that this only runs the human population component and that most users should read our fully worked example to run a full simulation.

We use the null (constant) model of human demography (\(H\) constant for all time). While \(H\) does not appear in the equations above, it must be specified as the model of bloodfeeding (\(\beta\)) relies on \(H\) to compute consistent values.

The Long Way

Here, we build a model step-by-step.

nStrata <- 3
H <- c(100, 500, 250)
residence = rep(1,3) 
searchWtsH = rep(1,3) 
b <- 0.55
c1 <- 0.05
c2 <- 0.25
r1 <- 1/250
r2 <- 1/50
TaR <- matrix(data = 1, nrow = 1, ncol = nStrata)

m20 <- 1.5
h <- r2*m20
m10 <- h/r1

EIR <- rep(h/b, 3) 
params <- make_parameters_xde()
params$nStrata = nStrata
params$nVectors = 1 
params$nHosts = 1
params$nPatches = 1 
params$EIR = list()

fF_eir = function(EIR){
  EIR = as.vector(EIR) 
  return(function(t, bday=0, scale=1){EIR})
} 
F_eir = fF_eir(EIR)

params = make_parameters_demography_null(pars = params, H=H) 

params = setup_BloodFeeding(params, 1, 1, residence=residence, searchWts=searchWtsH) 
params$BFpar$TaR[[1]][[1]]=TaR
params = make_parameters_X_hMoI(pars = params, b = b, c1 = c1, c2 = c2, r1 = r1, r2 = r2)
params = make_inits_X_hMoI(pars = params, rep(m10, nStrata), rep(m20, nStrata)) 
params = make_indices(params)
y0 <- get_inits(params)

out <- deSolve::ode(y = y0, times = c(0, 365), func = xDE_diffeqn_cohort, 
                    parms = params, method = 'lsoda', F_eir = F_eir) 
out1 <- out

colnames(out)[params$ix$X[[1]]$m1_ix+1] <- paste0('m1_', 1:params$nStrata)
colnames(out)[params$ix$X[[1]]$m2_ix+1] <- paste0('m2_', 1:params$nStrata)

out <- as.data.table(out)
out <- melt(out, id.vars = 'time')
out[, c("Component", "Strata") := tstrsplit(variable, '_', fixed = TRUE)]
out[, variable := NULL]

ggplot(data = out, mapping = aes(x = time, y = value, color = Strata)) +
  geom_line() +
  facet_wrap(Strata ~ Component, scales = 'free') +
  theme_bw()

Using Setup 

Xo = list(b=0.55, c1=0.05, c2=0.25, r1=1/250, r2=1/50, m20=1.5)
h = with(Xo, r2*m20) 
Xo$m10 = with(Xo, h/r1)  
Hpop = c(100, 500, 250)
xde_setup_cohort(F_eir, Xname="hMoI", HPop=Hpop, Xopts = Xo) ->test_hMoI
xde_solve(test_hMoI, 365, 365)$outputs$orbits$deout -> out2
approx_equal(out2, out1)
#>      time   X1   X2   X3   X4   X5   X6
#> [1,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [2,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE