Incidence Decay and Exponential Adjustment (IDEA)

This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013). See details for implementation notes.

Usage

idea(cases, mu)

Arguments

cases: Vector of case counts. The vector must be of length at least two and only contain positive integers.
mu: Mean of the serial distribution. This must be a positive number. The value should match the case counts in time units. For example, if case counts are weekly and the serial distribution has a mean of seven days, then mu should be set to 1. If case counts are daily and the serial distribution has a mean of seven days, then mu should be set to 7.

Value

An estimate of the basic reproduction number (R0).

Details

This method is closely related to that implemented in id(). The method is based on an incidence decay model. The estimate of R0 is the value which minimizes the sum of squares between observed case counts and case counts expected under the model.

This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic. The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known. The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended. Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.

References

Fisman et al. (PloS One, 2013)

Examples

# Weekly data.
cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)

# Obtain R0 when the serial distribution has a mean of five days.
idea(cases, mu = 5 / 7)
#> [1] 1.419546

# Obtain R0 when the serial distribution has a mean of three days.
idea(cases, mu = 3 / 7)
#> [1] 1.233927