sir: Compartmental epidemiological models

In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description

Simple SIR-type models implemented in various ways.

Usage

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)

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)

Arguments

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.

Details

sir() producees a ‘pomp’ object encoding a simple seasonal SIR model. 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.

Value

These functions return ‘pomp’ objects containing simulated data.

See Also

Other pomp examples: blowflies, dacca, gompertz, measles, ou2, ricker, rw2, verhulst

Examples

plot(sir())
coef(sir())

plot(sir2())
plot(simulate(window(sir2(),end=3)))
coef(sir2())

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.