This function implements a least squares estimation method of R0 due to +Fisman et al. (PloS One, 2013). See details for implementation notes.
+Arguments
+ + +- cases +
Vector of case counts. The vector must be non-empty 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
mushould be set to1. If case counts are daily and the serial +distribution has a mean of seven days, thenmushould be set to7.
+
+
Details
+The method is based on a straightforward incidence decay model. The estimate +of R0 is the value which minimizes the sum of squares between observed case +counts and cases 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.
+See also
+idea() for a similar method.
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.
+id(cases, mu = 5 / 7)
+#> [1] 1.245734
+
+# Obtain R0 when the serial distribution has a mean of three days.
+id(cases, mu = 3 / 7)
+#> [1] 1.14092
+