hubEnsemblesManuscript/README.Rmd at master · hubverse-org/hubEnsemblesManuscript · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
# Please put your title here to include it in the file below.
Title <- "Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R,"
```

# hubEnsemblesManuscript

[![DOI](https://zenodo.org/badge/756408646.svg)](https://doi.org/10.5281/zenodo.17259775)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hubverse-org/hubEnsemblesManuscript/master?urlpath=rstudio)


This repository contains the data and code for our paper:

> L. Shandross, E. Howerton, L. Contamin, et al., "Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R," *Statistics in Medicine* 45, no. 1-2 (2026): e70333, https://doi.org/10.1002/sim.70333.


### How to cite

Please cite this compendium as:

> Li Shandross, Emily Howerton, Lucie Contamin, Harry Hochheiser, Anna Krystalli, Consortium of Infectious Disease Modeling Hubs, Nicholas G. Reich, Evan L. Ray (in prep).
> *Compendium of R code and data for Multi-model ensembles in infectious disease and public health: Methods, interpretation, and implementation in R*.
> Accessed 06 Oct 2025. Online at <https://doi.org/10.5281/zenodo.17259775>

## Contents

The **analysis** directory contains:

  - [:file\_folder: paper](/analysis/paper): Quarto source document
    for manuscript. Includes code to reproduce the figures and tables
    generated by the analysis. It also has a rendered version,
    `hubEnsembles_manuscript.html`, suitable for reading (the code is replaced by figures
    and tables in this file)
  - [:file\_folder: data](/analysis/data): Data used in the analysis.
  - [:file\_folder: figures](/analysis/figures): Plots and other
    illustrations


## How to run in your browser or download and run locally

This research compendium has been developed using the statistical programming
language R. To work with the compendium, you will need
installed on your computer the [R software](https://cloud.r-project.org/)
itself and optionally [RStudio Desktop](https://rstudio.com/products/rstudio/download/).

You can download the compendium as a zip from from this URL:
[master.zip](/archive/master.zip). After unzipping:

- Open the `.Rproj` file in RStudio
- Run `devtools::install(dependencies = TRUE)` to ensure you have the packages this analysis depends on (also listed in the
[DESCRIPTION](/DESCRIPTION) file). You might need to install `devtools` first by running `install.packages("devtools")`.
  - A `renv.lock` with the exact versions of packages used is also present in the repository
- Finally, open `analysis/paper/hubEnsembles_manuscript.qmd` and knit to produce the `hubEnsembles_manuscript.html`, or run `rmarkdown::render("analysis/paper/hubEnsembles_manuscript.qmd")` in the R console

### Licenses

**Text and figures :**  [CC-BY-4.0](http://creativecommons.org/licenses/by/4.0/)

**Code :** See the [DESCRIPTION](DESCRIPTION) file

**Data :** [CC-0](http://creativecommons.org/publicdomain/zero/1.0/) attribution requested in reuse