smooth_estimates

Applies temporal smoothing to reproduction number estimates using a centered
moving average window. Handles missing and infinite values appropriately.

Provides methods to estimate serial intervals and time-varying case
reproduction numbers from infectious disease outbreak data. Serial intervals measure
the time between symptom onset in linked transmission pairs, while case reproduction
numbers quantify how many secondary cases each infected individual generates
over time. These parameters are essential for understanding transmission
dynamics, evaluating control measures, and informing public health responses.
The package implements the maximum likelihood framework from Vink et al. (2014)
<doi:10.1093/aje/kwu209> for serial interval estimation and the retrospective
method from Wallinga & Lipsitch (2007) <doi:10.1098/rspb.2006.3754> for
reproduction number estimation. Originally developed for scabies transmission
analysis but applicable to other infectious diseases including influenza,
COVID-19, and emerging pathogens. Designed for epidemiologists, public health
researchers, and infectious disease modelers working with outbreak surveillance
data.

Kylie Ainslie

mitey

Serial Interval and Case Reproduction Number Estimation

smooth_estimates function

<dl><dt>r_estimate</dt>
<dd>numeric vector; reproduction number estimates to smooth.
Can contain NA or infinite values</dd>
<dt>window</dt>
<dd>integer; size of the smoothing window in time units. Window is
centered around each point</dd></dl>

Arguments

Apply Moving Average Smoothing to R Estimates — smooth_estimates

<dl>

<dt>r_estimate</dt>
<dd>numeric vector; reproduction number estimates to smooth.
Can contain NA or infinite values</dd>


<dt>window</dt>
<dd>integer; size of the smoothing window in time units. Window is
centered around each point</dd>

</dl>

smooth_estimates: Apply Moving Average Smoothing to R Estimates

Description

Usage

Value

Arguments