---
title: Case studies
description: "A place to document how epinowcast has been used"
author: 
output:
  bookdown::html_vignette2:
    fig_caption: yes
    code_folding: show
pkgdown:
  as_is: true

csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa-numeric-superscript-brackets.csl
vignette: >
  %\VignetteIndexEntry{Package use cases}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```

The goal of this vignette is to provide a place to document how `{epinowcast}` has been applied in real-world settings. 
To start, we will document a few known use cases, many of which the authors are directly involved in. 
We hope this can both inspire future users to apply the methods to similar use cases of their own, and those currently or previously using `{epinowcast}` to document it here. This should also motivate and help prioritize future development. If you are using `{epinowcast}` for a real-world application, please consider opening a PR to add a description of the case study to this table. 

The table below contains columns to provide information on the following variables related to the use case:
`Pathogen`: Pathogen of interest in the use case, e.g. COVID-19.
`Data type(s)`: A brief description of the data source(s) used e.g. individual level line-list clinical case data
`Purpose`: Brief description of the purpose of using `{epinowcast}` e.g. research or real-time response
`Location`: Specific geographic location and associated granularity of the data to e.g. counties in the United States
`Organization type`: Type of organization doing the analysis e.g. academic, federal government, local health department
`Links`: Include here any links to manuscripts/pre-prints and github repositories describing and applying the analysis, e.g. [Manuscript](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012021#sec014)


|   Description   | Pathogen | Data type(s)  | Purpose | Location | Organization type | Links
|-----|---|---|---|---|---|--|
| Nowcasts of COVID-19 hospital admissions in Germany | COVID-19 | Counts of hospital admissions by date of positive test | Real-time response | National and state-level in Germany | Academic  | [Pre-print](https://epiforecasts.io/eval-germany-sp-nowcasting/paper.pdf), [Github repo]( https://github.com/epiforecasts/eval-germany-sp-nowcasting/), [Report](https://epiforecasts.io/eval-germany-sp-nowcasting/real-time-method-comparison/) |
| Generative Bayesian modelling to nowcast R(t) from line-list data with missing symptom onset date | COVID-19 | Individual line-list hospitalizations | Research | National level data in Switzerland | Academic | [Manuscript]( https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012021#sec014), [Github repo](https://github.com/adrian-lison/generative-nowcasting)|
| Nowcasting cases of norovirus in England in winter 2023-2024 | Norovirus | Counts of norovirus positive laboratory reports | Evaluation for real-time response | National level data in England | Federal government | [Pre-print](https://www.medrxiv.org/content/10.1101/2024.07.19.24310696v1) |