simulate_SIR_agents: Simulate SIR data according to a chain Binomial

In skgallagher/EpiCompare: A Pipeline to Analyze and Compare Infectious Disease Epidemics

Description

Simulate SIR data according to a chain Binomial

Usage

simulate_SIR_agents(n_sims, n_time_steps, beta, gamma, init_SIR)

Arguments

n_sims	number of times to run simulation
n_time_steps	number of total time steps (will use 0 to n_time_steps -1 inclusive)
beta	infection parameter for SIR chain Binomial. See details
gamma	recovery paraemter for SIR chain Binomial. See details
init_SIR	vector of (S0, I0, R0) the number of agents initially in the Susceptible, Infected, and Recovered state, respectively. The sum of this will be used as the number of agents

Details

For each simulation i, agent A_{t,k} (the kth agent at time t) will update according to the following where the states are denoted S=0,I=1,R=2. The update follows a Bernoulli draw based on the agent's current state. Specifically,

A_{t,k}| S_{t-1}, I_{t-1} \sim ≤ft \{\begin{array}{ll} \textnormal{Bernoulli} ≤ft ( p_{t-1}\right ) & \textnormal{ if } A_{t-1,k} = 0 \\ 1 + Bernoulli(γ) & \textnormal{ if } A_{t-1,k} = 1 \\2 & \textnormal{ otherwise} \end{array} \right .

If the agent was infectious at time t=0 then tI <= 0 . If the agent never becomes infectious then tI = NA. If the agent never recovers or is recovered from time 0 on then tR = NA. Otherwise we assume the agent is susceptible.

Value

The the output is a data.frame with columns agent_id, init_state, sim_num, tI, tR. The size is (n_agents x n_sims) x 4.

Examples

sims_data <- simulate_SIR_agents(n_sims = 2, n_time_steps = 5,
                                 beta = .5, gamma = .1, init_SIR = c(9,1,0))
dim(sims_data)

skgallagher/EpiCompare documentation built on Sept. 14, 2021, 5:45 a.m.