From 16b8e23ecb7810df3592349bb8fbdf91f5f89b79 Mon Sep 17 00:00:00 2001
From: hannajankowski <hkj@yorku.ca>
Date: Fri, 29 Jul 2022 10:47:02 -0400
Subject: Add files via upload

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 man/IDEA.Rd | 46 ++++++++++++++++++++++++++++++++++++++++++++++
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/IDEA.R
+\name{IDEA}
+\alias{IDEA}
+\title{IDEA method}
+\usage{
+IDEA(NT, mu)
+}
+\arguments{
+\item{NT}{Vector of case counts}
+
+\item{mu}{Mean of the serial distribution (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{IDEA} returns a list containing the following components:  \code{Rhat} is the estimate of R0  and \code{inputs} is a list of the original input variables \code{NT, mu}.
+}
+\description{
+This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013).    See details for implementation notes.
+}
+\details{
+This method is closely related to that implemented in \code{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 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.
+}
+\examples{
+
+## ===================================================== ##
+## Illustrate on 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
+res1 <- IDEA(NT=NT, mu=5/7)	
+res1$Rhat
+## obtain Rhat when serial distribution has mean of three days
+res2	<- IDEA(NT=NT, mu=3/7)	
+res2$Rhat
+
+## ========================================================= ##
+## Compute Rhat using only the first five weeks of data      ##
+## ========================================================= ##
+
+
+res3 <- IDEA(NT=NT[1:5], mu=5/7)		# serial distribution has mean of five days
+res3$Rhat
+}
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