utah_covid_data

Daily COVID-19 epidemic indicators for the state of Utah, obtained from the
Utah Department of Health COVID-19 dashboard.

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

utah_covid_data function

A data frame with 365 rows and 5 variables:<dl>
<dt>Date</dt>
<dd>Date of the observation (Date).</dd><dt>Daily.Cases</dt>
<dd>Number of newly reported COVID-19 cases (numeric).</dd><dt>Smoothed.3.Day.Moving.Average</dt>
<dd>Smoothed daily cases using a 3-day moving average (numeric).</dd><dt>X3.Day.Moving.Average</dt>
<dd>Alternative 3-day moving average of daily cases (numeric).</dd><dt>Status</dt>
<dd>Indicator of reporting or epidemic status (character or factor).</dd>
</dl>

Format

Utah COVID-19 epidemic trends — utah_covid_data

A data frame with 365 rows and 5 variables:<dl>
<dt>Date</dt>
<dd>Date of the observation (Date).</dd>

<dt>Daily.Cases</dt>
<dd>Number of newly reported COVID-19 cases (numeric).</dd>

<dt>Smoothed.3.Day.Moving.Average</dt>
<dd>Smoothed daily cases using a 3-day moving average (numeric).</dd>

<dt>X3.Day.Moving.Average</dt>
<dd>Alternative 3-day moving average of daily cases (numeric).</dd>

<dt>Status</dt>
<dd>Indicator of reporting or epidemic status (character or factor).</dd>


</dl>

utah_covid_data: Utah COVID-19 epidemic trends

Description

Usage

Arguments

Format

Examples