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Diffstat (limited to 'man/ID.Rd')
| -rw-r--r-- | man/ID.Rd | 44 |
1 files changed, 21 insertions, 23 deletions
@@ -9,41 +9,39 @@ ID(NT, mu) \arguments{ \item{NT}{Vector of case counts.} -\item{mu}{Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts -are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one If case -counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.} +\item{mu}{Mean of the serial distribution. This needs to match case counts +in time units. For example, if case counts are weekly and the +serial distribution has a mean of seven days, then \code{mu} should +be set to one. If case counts are daily and the serial distribution +has a mean of seven days, then \code{mu} should be set to seven.} } \value{ \code{ID} returns a single value, the estimate of R0. } \description{ -This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013). -See details for implementation notes. +This function implements a least squares estimation method of R0 due to +Fisman et al. (PloS One, 2013). See details for implementation notes. } \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. +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. +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. } \examples{ -## ===================================================== ## -## Illustrate on weekly data ## -## ===================================================== ## - +# Weekly data: NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) -## obtain Rhat when serial distribution has mean of five days -ID(NT=NT, mu=5/7) -## obtain Rhat when serial distribution has mean of three days -ID(NT=NT, mu=3/7) -## ========================================================= ## -## Compute Rhat using only the first five weeks of data ## -## ========================================================= ## +# Obtain R0 when the serial distribution has a mean of five days. +ID(NT, mu = 5 / 7) -ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days +# Obtain R0 when the serial distribution has a mean of three days. +ID(NT, mu = 3 / 7) } |