SIRS2nets: SIRS model in two networks with the same nodes

In leb-fmvz-usp/epinemo: An R Package for EPIdemiological NEtwork MOdels

SIRS model in two networks with the same nodes

Description

A function to run the simulation of disease spread in two networks with the same nodes using the SIRS (Susceptible-Infected-Recovered-Susceptible) model.

Usage

SIRS2nets(
  A1,
  A2,
  pspread1,
  pspread2,
  tSim,
  I,
  tImin,
  tImax,
  R,
  tRmin,
  tRmax,
  Control
)

Arguments

A1	The adjacency matrix of network 1
A2	The adjacency matrix of network 2
pspread1	Probability of disease spread from an infected to a susceptible node in network 1
pspread2	Probability of disease spread from an infected to a susceptible node in network 2
tSim	Simulation time
I	Vector of infected nodes (initial condition)
tImin	Minimum time a node remains infected
tImax	Maximum time a node remains infected
R	Vector of recovered nodes (initial condition)
tRmin	Minimum time a node remains immune
tRmax	Maximum time a node remains immune
Control	Vector of nodes under a control strategy

Details

This function runs the simulation of disease spread in two networks with the same nodes using the SIRS model. For each time step, the vectors of infected, susceptible and recovered nodes are updated, considering that there are probabilities pspread1 and pspread2 of disease spread in networks 1 and 2, respectively. Thus, infection can be transmitted by either network. Infected nodes remain infected during a time randomly sampled between tImin and tImax. Recovered nodes remain immune during a time randomly sampled between tRmin and tRmax. Nodes under a control strategy (Control) are not susceptible to infection.

Value

A list of

M_Sim_I	a matrix of infected nodes for each time step.
M_Sim_R	a matrix of recovered nodes for each time step.

References

[1] Ossada R, Grisi-Filho JHH, Ferreira F, Amaku M (2013). "Modeling the Dynamics of Infectious Diseases in Different Scale-Free Networks with the Same Degree Distribution." Advanced Studies in Theoretical Physics, 7, 759-771. doi: 10.12988/astp.2013.3674

[2] Ossada R (2015). "Modelagem de Medidas de Controle em Redes de Movimentacao de Animais." PhD Thesis. Sao Paulo, School of Veterinary Medicine, University of Sao Paulo. doi: 10.11606/T.10.2015.tde-06112015-111048

Examples

# Generate two arbitrary 200 by 200 adjacency matrix with zeros and ones
# Remove loops
A1 <- matrix(rbinom(200 * 200, 1, 0.1), ncol = 200, nrow = 200)
diag(A1) <- 0

A2 <- matrix(rbinom(200 * 200, 1, 0.05), ncol = 200, nrow = 200)
diag(A2) <- 0

# Setting the parameters
pspread1 <- 0.05
pspread2 <- 0.1
tImin <- 5
tImax <- 8
tRmin <- 1
tRmax <- 10
tSim <- 100

# Setting the initial conditions for infected, recovered and controlled nodes
num_infected <- 2 # initial number of infected nodes
I <- rep(x = 0, times = nrow(A))
I[1:num_infected] <- 1
I <- sample(I)
R <- rep(x = 0, times = nrow(A))
Control <- rep(x = 0, times = nrow(A))
  
# Run the simulation
sim2nets <- SIRS2nets(A1 = A1, A2 = A2, pspread1 = pspread1, pspread2 = pspread2, tSim = tSim,
                  I = I, tImin = tImin, tImax = tImax, 
                  R = R, tRmin = tRmin, tRmax = tRmax, Control = Control)

# Plot the prevalence over time
plot(colMeans(sim2nets[[1]]>0), xlab = "Time", ylab = "Prevalence")
                                                                                                  

leb-fmvz-usp/epinemo documentation built on Nov. 27, 2022, 10:58 p.m.