generate_case_bootstrap

Creates a bootstrap sample by resampling individual cases with replacement,
then reconstructing daily incidence counts. This maintains the temporal
distribution while introducing sampling variation for uncertainty estimation.

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

generate_case_bootstrap function

<dl><dt>incidence</dt>
<dd>numeric vector; daily case counts (non-negative integers)</dd></dl>

Arguments

Generate Bootstrap Sample of Case Incidence — generate_case_bootstrap

<dl>

<dt>incidence</dt>
<dd>numeric vector; daily case counts (non-negative integers)</dd>

</dl>

generate_case_bootstrap: Generate Bootstrap Sample of Case Incidence

Description

Usage

Value

Arguments