Simulate_contact_control1: Simulation of the epidemic control process with weibull...
In holaanna/contactsimulator: Contact model simulator

Description Usage Arguments Details References See Also Examples

View source: R/simulation_control_weibul.R

Epidemic simulation using the contact type model with the Australian control strategies.

Simulate_contact_control1(f_rast = NULL, b_rast = NULL,
  farm_pos_cat = NULL, vis_int_per_cat = NULL, param, grid_lines,
  pop_grid, grid_size = 500, age_level = c(1, 1), age_dist = c(1, 0),
  m_start = 1, t_b = 1e+05, t_max = 1000, t_intervention = 1e+05,
  t_obs = 3703, EI_model = 1, kern_model = 4, rad = 1000,
  sweep_prop = c(0.5, 0.5), back_p = c(0.7, 0.5), rate_det = 0.3,
  int_det = c(30, 90, 180), nb_in_b = 1, nb = 30, leav = c(3, 6))

`f_rast`	Population density of the grid a farm plant resides. This is filled from bottom to top, then left to right.
`b_rast`	Population density of the grid a backyard plant resides. This is filled from bottom to top, then left to right.
`farm_pos_cat`	A data frame of the intial conditon: ro The row at which the cell containing a farm lies in co The column at which the cell containing a farm lies in cat The category that the cell/farm belongs to vis_int Revisit interval tim_lst_pos Time of last positive nb_round Number of round of revisit sweep Decide whether to carry out the sweep over the farm: 0->no and 1->yes
`vis_int_per_cat`	A data frame of the visiting intervale for the alternative strategy: cat The category that the cell/farm belongs to vis_int Revisit interval
`param`	Indicating a data frame containing a vector of parameters including: epsion The primary infection rate. See `func_time_beta` beta_0 Baseline or average transmission rate. See `func_time_beta` beta_1 Amplitude of the seasonality. See `func_time_beta` alpha1,alpha2 The dispersal kernel parameters. mu_lat,var_lat mean and variance of the latent period. See `E_to_I` for details. t0 Time at which the primary source became active . omega Period of the forcing. See `func_time_beta` gama The mean proportion of short range dispersal events. .
`pop_grid`	Population density of the grid a case resides. This is filled from bottom to top, then left to right.
`grid_size`	Grid resolution //@inheritParams circle_line_intersections
`age_level`	Vectors of age level and the propportion of each age group respectively. See details.
`age_dist`	Vectors of age level and the propportion of each age group respectively. See details.
`m_start`	The size of initial cases. Default is 1.
`t_b`	Time representing the end of the baseline programme or the start of the alternative programme
`t_max`	Final observation time.
`t_intervention`	Start of the intervention if any.
`t_obs`	End of the observation time.
`EI_model`	Take integer values to specify the type of model used for the latent period. See `E_to_I`
`kern_model`	Take integer values to specify the type of dispersal kernel used. See `Samp_dis`
`rad`	Sweep radius
`sweep_prop`	A two element vector represention the proportion of plantation to consider for the sweep
`back_p`	A two element vector represention thes Backyard assessment proportion within sweep radius:
`rate_det`	Detection rate. 1st element Detection rate for the grower 2nd element Detection rate for the expert
`int_det`	Three elements vector representing the revisit intervals:
`nb_in_b`	The number of initial plants infected in category B farms
`nb`	The scaling factor of backyards
`leav`	The number of leaves to consider as a measurement for removal: 3 for expert to have a 100 detection

Simulate_contact_control1 provide the simulation of the epidemic process with the Australian BBTV management plan.

\insertRef

KR08contactsimulator \insertRefMee11contactsimulator

Simulate_contact_control

f<- system.file("external/rast_SEQ.tif", package="contactsimulator")
rast<- raster(f)
 size<- raster::res(rast)[1]
# Extract infos on the grid


n_row_grid=nrow_grid=raster::nrow(rast)
n_col_grid=ncol_grid=raster::ncol(rast)
grid_size=raster::res(rast)[1]     # Resolution

n_line=(nrow_grid+1) + (ncol_grid +1)  # Number of grid  lines

x_min=raster::xmin(rast)  # min max of the bounding box
x_max=raster::xmax(rast)

y_min=raster::ymin(rast)
y_max=raster::ymax(rast)

da=as.data.frame(pointsinaustraliangrid)

pop_per_grid=round(raster::values(rast)*size^2)
pop_per_grid[is.na(pop_per_grid)]=0
mat=matrix(pop_per_grid,nrow = nrow_grid, byrow = TRUE)
pop_grid=apply(mat,2,rev)     # population per grid

# Structure of the grid
x=seq(x_min,x_max,grid_size)
y=seq(y_min,y_max,grid_size)

grid_lines=array(0,c(n_line,6))
for(i in 1:n_line){
  if(i<=(nrow_grid +1)){
    grid_lines[i,]=c(i,1,x[1],y[i],x[length(x)],y[i])
  }
  else{
    grid_lines[i,]=c(i,2,x[i-length(y)],y[1],x[i-length(y)],y[length(y)])
  }
}

grid_lines=as.data.frame(grid_lines)
colnames(grid_lines)<- c("indx","orient_line","coor_x_1","coor_y_1","coor_x_2","coor_y_2")
circle_x=2022230
circle_y=-3123109
r=10000
# Simulation with exponential kernel
alpha<- 30; beta<- 0.012; epsilon<- 0.02; omega<- 0.12; mu_lat<- 30; var_lat<- 20; t0<- 0; c<- 20; b1<-0; gama<- 0.5
param=data.frame(alpha1=alpha, alpha2=alpha, beta=beta, epsilon=epsilon, omega=omega, mu_lat=mu_lat, var_lat=var_lat, t0=t0, c=c, b1=b1, gama=gama)
f<- system.file("external/rast_farms_SEQ.tif", package="contactsimulator")
f_rast<- raster(f)
pop=round(raster::values(f_rast)*size^2)
pop[is.na(pop)]=0
mat=matrix(pop,nrow = nrow_grid, byrow = TRUE)
f_rast=apply(mat,2,rev)
y<- system.file("external/rast_backy_SEQ.tif", package="contactsimulator")
b_rast<- raster(y)
pop=round(raster::values(b_rast)*size^2)
pop[is.na(pop)]=0
mat=matrix(pop,nrow = nrow_grid, byrow = TRUE)
b_rast=apply(mat,2,rev)