sir_models {pomp} | R Documentation |
Simple SIR-type models implemented in various ways.
sir(gamma = 26, mu = 0.02, iota = 0.01, beta1 = 400, beta2 = 480, beta3 = 320, beta_sd = 0.001, rho = 0.6, pop = 2100000, S_0 = 26/400, I_0 = 0.001, R_0 = 1 - S_0 - I_0, t0 = 0, times = seq(from = t0 + 1/52, to = t0 + 4, by = 1/52), seed = 329343545) sir2(gamma = 24, mu = 1/70, iota = 0.1, beta1 = 330, beta2 = 410, beta3 = 490, rho = 0.1, pop = 1e+06, S_0 = 0.05, I_0 = 1e-04, R_0 = 1 - S_0 - I_0, t0 = 0, times = seq(from = t0 + 1/12, to = t0 + 10, by = 1/12), seed = 1772464524)
gamma |
recovery rate |
mu |
death rate (assumed equal to the birth rate) |
iota |
infection import rate |
beta1, beta2, beta3 |
seasonal contact rates |
beta_sd |
environmental noise intensity |
rho |
reporting efficiency |
pop |
overall host population size |
S_0, I_0, R_0 |
the fractions of the host population that are susceptible, infectious, and recovered, respectively, at time zero. |
t0 |
zero time |
times |
observation times |
seed |
seed of the random number generator |
sir()
producees a ‘pomp’ object encoding a simple seasonal SIR model with simulated data.
Simulation is performed using an Euler multinomial approximation.
sir2()
has the same model implemented using Gillespie's algorithm.
This and similar examples are discussed and constructed in tutorials available on the package website.
These functions return ‘pomp’ objects containing simulated data.
Other pomp examples: blowflies
,
dacca
, ebola
,
gompertz
, measles
,
ou2
, ricker
,
rw2
, verhulst
po <- sir() plot(po) coef(po) po <- sir2() plot(po) plot(simulate(window(po,end=3))) coef(po)