#Poisson Networks
#2nx = (1-a)nx + 10anx
#n = 1/9
#So about 11% need to increase by 10
library(metaflu)
library(ggplot2)
library(dplyr)
library(doMC)
library(tidyr)
set.seed(17)
basic <- function(farm_size, farm_number){
initial_cond <- basic_nodes(farm_size, farm_number)
infected_patches <- sample(seq_len(nrow(initial_cond)), 1)
initial_cond[infected_patches, 2] <- 1
initial_cond[infected_patches, 1] <- initial_cond[infected_patches, 1] - 1
parms = list(
beta = 1.44456, #contact rate for direct transmission
gamma = 0.167, #recovery rate
mu = 0, #base mortality rate
alpha = 0.4, #disease mortality rate
phi = 0, #infectiousness of environmental virions
eta = 0, #degradation rate of environmental virions
nu = 0.00, #uptake rate of environmental virion
sigma = 0, #virion shedding rate
omega = 0.03, #movement rate
rho = 0.85256, #contact nonlinearity 0=dens-dependent, 1=freq-dependent
lambda = 0, #force of infection from external sources
tau_crit = 0, #critical suveillance time
I_crit = 0, #threshold for reporting
pi_report = 0, #reporting probability
pi_detect = 0, #detection probability
chi = make_net(network_type = "smallworld",
network_parms = list(dim = 1, size = farm_number, nei = 2.33, p = 0.0596, multiple = FALSE, loops = FALSE)),
stochastic_network = TRUE
)
x <- mf_sim(init = initial_cond, parameters = parms, times=0:100, n_sims = 20, return_array=TRUE)
return(x)
}
#registerDoMC(cores=20)
basic_results <- basic(15, 100)
saveRDS(basic_results, "basic_results.rds")
grown <- function(farm_size, farm_number){
set.seed(123)
initial_cond <- growth_nodes(farm_size, farm_number)
infected_patches <- sample(seq_len(nrow(initial_cond)), 1)
initial_cond[infected_patches, 2] <- 1
initial_cond[infected_patches, 1] <- initial_cond[infected_patches, 1] - 1
parms = list(
beta = 0.0081, #contact rate for direct transmission
gamma = 0.167, #recovery rate
mu = 0, #base mortality rate
alpha = 0.4, #disease mortality rate
phi = 0, #infectiousness of environmental virions
eta = 0, #degradation rate of environmental virions
nu = 0.00, #uptake rate of environmental virion
sigma = 0, #virion shedding rate
omega = 0.03, #movement rate
rho = 0, #contact nonlinearity 0=dens-dependent, 1=freq-dependent
lambda = 0, #force of infection from external sources
tau_crit = 0, #critical suveillance time
I_crit = 0, #threshold for reporting
pi_report = 0, #reporting probability
pi_detect = 0, #detection probability
chi = make_net(network_type = "smallworld",
network_parms = list(dim = 1, size = farm_number, nei = 2.33, p = 0.0596, multiple = FALSE, loops = FALSE)),
stochastic_network = TRUE
)
x <- mf_sim(init = initial_cond, parameters = parms, times=0:100, n_sims = 100, return_array=FALSE)
return(x)
}
grown_results2 <- grown(15,100)
saveRDS(grown_results, "grown_results.rds")
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