run_simulation: Runs the spatial epidemic simulation
In qleclerc/epicspatial: Flexible Spatial Modelling of Epidemics

Description Usage Arguments Details Value Examples

Simulates an epidemic using the provided spatial dataset, spatial kernel, contact matrix, and infection parameters.

1
2
3

run_simulation(spatial_data, D, contact_mat, beta, sigma = 1/2.6,
  stoch = FALSE, step = 1, start_area = NA, start_num = 10,
  t_max = 100)

`spatial_data`	The spatial dataset containing the population data.
`D`	The expanded kernel matrix to use for FOI calculation (generated by the `calc_beta` function).
`contact_mat`	The contact matrix for mixing between age groups.
`beta`	The beta value for the epidemic (calculated from a given R0 using the `calc_beta` function).
`sigma`	The recovery rate for the epidemic (must match the one used to calculate beta from R0 using the `calc_beta` function).
`stoch`	Logical. If TRUE, the simulation is stochastic.
`step`	Size of time step for stochastic simulation, in days (default is 1 day).
`start_area`	Where to start the epidemic. Default: area with highest population density. If the spatial dataset is a SpatialPolygonsDataFrame, you can specify a string indicating the name of your desired starting area. Alternatively, no matter which type of spatial dataset you have, you can indicate the index of you desired starting area.
`start_num`	Number of infected individuals to start the epidemic.
`t_max`	How many days to run the simulation for.

This functions requires specific objects to run. These can be generated using the prep_simulation function (e.g. if you want to simulate an epidemic using the spatial dataset "testdata", you must prep_simulation(test_data) first). The model used is an SIR model, where individuals can be either Susceptible, Infected or Recovered with regards to the disease. This assumes that Infected individuals are infectious, and that Recovered individuals are immune and cannot be reinfected.

Returns one dataframe object containing the estimates of Susceptible, Infected and Recovered individuals for each time step.

#Create a spatial dataset:
test_data = raster(nrow=10, ncol=10, xmn=1, xmx=100000, ymn=1, ymx=100000)
values(test_data) = runif(100, 1, 1000)

#Calculate the parameters for the simulation:
prep_simulation(test_data)

#Run the simulation:
results = run_simulation(test_data, expanded_D, contact_mat, beta)