cases_to_SIR: Impute Recovered counts for the SIR model

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

Description

Impute Recovered counts for the SIR model

Usage

cases_to_SIR(data, par, method = "chain-binomial")

Arguments

data	data frame or grouped data frame with the following columns t time confirmed number of cumulative confirmed cases at time t in the group N population size, which is used as the number of susceptible (minus the initial infections)
par	named vector of parameters
method	Currently default is "chain-binomial". See details. More methods to come.

Details

For the method "chain-binomial". Let the cumulative case counts at time t be J_t. Then the number of susceptibles is simply S_t = N - J_t. The number of infectious and recovered is imputed iteratively using random draws from a chain binomial based on the state sizes at the previous time step. Specifically, we assume I_{t_0} = J_{t_0} and R_{t_0} = 0, that is the initial number of recovered individuals is zero. Then for each t \in \{ t_0 + 1, t_0 + 2, …, T\} R_t = R_{t-1} + Binomial(I_{t-1}, γ) and I_t = J_t - R_t. Here (X0, X1, X2) = (S, I, R).

Value

the input data with the additional columns

X0

number of susceptible

X1

number of infectious

X2

number of recovered

Examples

  df <- data.frame(t = 0:4,
                    confirmed = c(0, 1, 3, 9, 9),
                    N = 10)
   out <- cases_to_SIR(data = df,
                       par = 1)

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