SEEDNet: Satellite Enhanced Epidemic Disease Network

SEEDNet has been designed to simulate outbreaks over a network created by combining satellite imagery, population data, and road network information.

Example datasets

While a smaller example dataset is included in inst/sampleData, the bigger examples can be found in this Google Drive link. They include:

  1. g_raw.RDA: For using with disnet_commuting()
  2. g.RDA: For using with disnet_sim_setup()
  3. sim_intermed.RDA: To run the disnet_simulation() function with.

Compartmental disease model

Discrete time, stochastic, metatpopulation model.

In case of influenza, the compartmental model is SEIR:

S - Susceptible E - Latent I - Infectious R - Recovered

Main functions

  1. disnet_commuting - Comuting function. Takes in network object (in graphml format), and calculates the commuting rates for all outgoing edges in network

  2. disnet_sim_setup - Simulation setup function. Takes in the graph file with commuting rates added to it, and preps it up for runnning simulations.

  3. disnet_simulate - Simulation function. This function runs the disease model simulations over the network.

library("disnet")

# Read in sample graph/network
f = system.file("sampleData", "g.rds", package = "disnet")
g = readRDS(f)

# calculate commuting rates over it
g_comm = disnet_commuting(g)

# select random node to seed infection in
set.seed(890)
nodes = igraph::vcount(g_comm)
seed_nd = igraph::vertex_attr(g_comm, "name", sample(1:nodes, 1))

# set up the network for simulations
for_sim = disnet_sim_setup(g_comm, seed_nd = seed_nd, output_dir = NA)

# run the simulations over the network
simres = disnet_simulate(sim_input = for_sim, sim_output_dir = NA)

simres2 = disnet_simulate(sim_input = for_sim, sim_output_dir = NA,
parallel = TRUE)

Sample datasets

  • g.RDS: The raw graphml/network object

To access the sample dataset:

f = system.file("sampleData", "g.rds", package = "disnet")
g = readRDS(f)