abc_calibration_params

Results from Approximate Bayesian Computation (ABC) calibration of an
SIR network model fitted to Utah COVID-19 incidence data.

datasets

Provides tools and pre-trained Machine Learning [ML] models for
calibration of Agent-Based Models [ABMs] built with the R package
'epiworldR'. Implements methods described in Najafzadehkhoei, Vega Yon,
Modenesi, and Meyer (2025) <doi:10.48550/arXiv.2509.07013>. Users can
automatically calibrate ABMs in seconds with pre-trained ML models,
effectively focusing on simulation rather than calibration. Bridges a gap
by allowing public health practitioners to run their own ABMs without the
advanced technical expertise often required by calibration.

Sima Najafzadehkhoei

epiworldRcalibrate

Fast and Effortless Calibration of Agent-Based Models using
Machine Learning

abc_calibration_params function

A named list with the following elements:<dl>
<dt>contact_rate</dt>
<dd>Posterior median of the contact rate.</dd><dt>recovery_rate</dt>
<dd>Posterior median of the recovery rate.</dd><dt>transmission_prob</dt>
<dd>Posterior median of the transmission probability.</dd><dt>R0</dt>
<dd>Basic reproduction number computed from posterior medians.</dd><dt>contact_rate_ci</dt>
<dd>95 percent credible interval for the contact rate.</dd><dt>recovery_rate_ci</dt>
<dd>95 percent credible interval for the recovery rate.</dd><dt>transmission_prob_ci</dt>
<dd>95 percent credible interval for the transmission probability.</dd><dt>calibration_time_seconds</dt>
<dd>Total runtime of the ABC calibration (seconds).</dd><dt>n_samples</dt>
<dd>Number of MCMC samples used in calibration.</dd><dt>burnin</dt>
<dd>Number of burn-in iterations discarded.</dd><dt>epsilon</dt>
<dd>ABC tolerance parameter.</dd><dt>seed</dt>
<dd>Random seed used for reproducibility.</dd><dt>posterior_samples</dt>
<dd>Matrix of post-burn-in accepted parameter samples.</dd><dt>acceptance_rate</dt>
<dd>Acceptance rate of the ABC-MCMC algorithm (percent).</dd>
</dl>

Format

ABC calibration results for COVID-19 SIR model — abc_calibration_params

A named list with the following elements:<dl>
<dt>contact_rate</dt>
<dd>Posterior median of the contact rate.</dd>

<dt>recovery_rate</dt>
<dd>Posterior median of the recovery rate.</dd>

<dt>transmission_prob</dt>
<dd>Posterior median of the transmission probability.</dd>

<dt>R0</dt>
<dd>Basic reproduction number computed from posterior medians.</dd>

<dt>contact_rate_ci</dt>
<dd>95 percent credible interval for the contact rate.</dd>

<dt>recovery_rate_ci</dt>
<dd>95 percent credible interval for the recovery rate.</dd>

<dt>transmission_prob_ci</dt>
<dd>95 percent credible interval for the transmission probability.</dd>

<dt>calibration_time_seconds</dt>
<dd>Total runtime of the ABC calibration (seconds).</dd>

<dt>n_samples</dt>
<dd>Number of MCMC samples used in calibration.</dd>

<dt>burnin</dt>
<dd>Number of burn-in iterations discarded.</dd>

<dt>epsilon</dt>
<dd>ABC tolerance parameter.</dd>

<dt>seed</dt>
<dd>Random seed used for reproducibility.</dd>

<dt>posterior_samples</dt>
<dd>Matrix of post-burn-in accepted parameter samples.</dd>

<dt>acceptance_rate</dt>
<dd>Acceptance rate of the ABC-MCMC algorithm (percent).</dd>


</dl>

abc_calibration_params: ABC calibration results for COVID-19 SIR model

Description

Usage

Arguments

Format

Examples