index.qmd

---
title: "Weekly COVID-19 Forecast for Utah"
date: last-modified
execute:
  echo: false
---

## Introduction

This COVID-19 forecast is published weekly.
Using case data published at the Utah DHHS [Coronavirus Dashboard](https://coronavirus.utah.gov/case-counts/), we calibrate an SIR connected model and simulate it with [epiworldR](https://github.com/UofUEpiBio/epiworldR) to generate the forecast.

The forecast was last updated on {{< meta date >}}.

## Overview of Observed Data
Utah DHHS publishes weekly surveillance data on their [Coronavirus Dashboard](https://coronavirus.utah.gov/case-counts/).
Below are the daily COVID-19 case counts from March 18, 2020 to {{< meta date >}}.

```{r}
# Download the Trends data from Utah DHHS
source("get-forecast-data.R")
data_url <- "https://coronavirus-dashboard.utah.gov/Utah_COVID19_data.zip"
target_file_regex <- "Trends_Epidemic+"
forecast_data <- get_forecast_data(data_url, target_file_regex)
# Check for errors
if (length(forecast_data) > 1) {
  # Plot the observed data
  plot(
    x = as.Date(forecast_data$Date),
    y = forecast_data$Daily.Cases,
    xlab = "Date",
    ylab = "Daily Cases",
    main = "Daily COVID-19 Cases in Utah"
  )
}
```

## Epiworld Forecast
We calibrate a SIR Connected model using the above data and run the model in epiworldR.
Here are the results of a single model run:
```{r}
library(epiworldR)
model_sir <- ModelSIRCONN(
  name              = "COVID-19",
  n                 = 50000,
  prevalence        = 0.0001,
  contact_rate      = 2,
  transmission_rate = 0.5,
  recovery_rate     = 1 / 3
)

# Printing Model Summary
summary(model_sir)
```

## Methodology
Here we describe the process used for calibrating the model and generating the forecast.

## Acknowledgements
Here we acknowledge the contributions to the forecast.