Reproducible results. Wastewater-based epidemiology framework:

Collaborative modeling for sustainable disease surveillance

Journal article: https://doi.org/10.1016/j.scitotenv.2025.178889

Repository:

Néstor DelaPaz-Ruíz, Ellen-Wien Augustijn, Mahdi Farnaghi, Shaheen A. Abdulkareem, Raul Zurita-Milla. September, 2024

Affiliation: Department of Geo-Information Process (GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands

About

This repository provides access to the materials (code-data), software-environments (NetLogo, Rstudio, Docker containers), and the steps for reproducing the results from the publication: Wastewater-based epidemiology framework: Collaborative modeling for sustainable disease surveillance.

The information below guides you to execute two NetLogo simulations. The main outputs are the figures representing the results of the mentioned publication.

Getting Started

Pre-requirements

Used computational environment:

```
Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22631) Processor: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz Logical processors: 12 Installed RAM 32.0 GB (31.6 GB usable) System type 64-bit operating system, x64-based processor

R version 4.3.1 (2023-06-16 ucrt) RStudio 2023.12.1

NetLogo 6.3 ```

You will need the following:

1. Data:

• Download or copy this repository to a local folder .

1. Open-source software:

Quick reproducibility (requires NetLogo/Rstudio experience):

• Install and run Git Bash. See: https://gitforwindows.org/
• Install and run NetLogo. See: https://ccl.northwestern.edu/netlogo/6.3
• Install Rstudio. See: https://dailies.rstudio.com/version/2023.12.1+402/
• NetLogo configuration: Copy the provided NetLogo.cfg file and replace it at the folder: C:\Program Files\NetLogo 6.3.0\app. In this way, you are free to use several processors. Please, refer to the following link: FAQ: How big can my model be? How many turtles, patches, procedures, buttons, and so on can my model contain?. After the NetLogo installation, make sure to execute the . ./code/newfiles.txt in Git Bash.

1. Libraries and extensions:

• NetLogo: The first time running the Netlogo file, you will get a windows to install the time extension. Follow the instructions to install it.
• Rstudio: The first time that you open the R project execute the renv::restore() in the R console to load the required libraries. You can explore the library requirements in the renv.lock file.

Quick reproducibility

This section is for Windows users familiar with NetLogo, Rstudio, and Git bash. Mac or Linux users should not try to reproduce results. Jump to the long-term reproducibility section, which uses Docker to reproduce results as the alternative for non-experienced users.

Steps for a quick reproduction:

1. In a new folder, execute the following commands in git bash:

bash git clone https://github.com/delapazruiz/ddw.framework.git

bash cd ddw.framework/

bash . ./code/newfiles.txt

1. Open the NetLogo (v. 6.3) file and run the experiment. In Tools/BehaviorSpace, select number of processors to run the experiment. (time: around 30 min)

First simulation (around 30 min):

bash s4.framework.wastewater.surveillance.nlogo

Second simulation (around 30 min):

bash s5.framework.wastewater.tss.loads.nlogo

1. Open the Rstudio project file and verify the library requirements (between 15-30 min).

R Project file:

bash ddw.framework.Rproj

Make sure that ‘renv’ is installed and loaded.

bash install.packages("renv")

Load the ‘renv’ library.

bash library(renv)

Run the following in the R console to install the required libraries.

bash renv::restore()

1. Open the Quarto file (.qmd) and render it to generate the report: (time: around 10 min)

bash Framework.wastewater.surveillance.qmd

Long-term reproducibility (Docker-Rstudio)

Pre-requirements

Used computational environment:

```
Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22631) Processor: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz Logical processors: 12 Installed RAM 32.0 GB (31.6 GB usable) System type 64-bit operating system, x64-based processor

NetLogo 6.3

Docker Desktop v.4.16.3 ```

Data:

```
You are expected to download this repo and run the NetLogo simulations:

s4.framework.wastewater.surveillance.nlogo (around 30 min) s5.framework.wastewater.tss.loads.nlogo (around 30 min) ```

Steps: Docker-Rstudio

Follow the next steps after you get the NetLogo .csv output files. Experience with docker is expected.

1. Verify that you are in the folder 'ddw.framework'. In git bash, execute:

bash docker run -p 8787:8787 -e ROOT=TRUE -e PASSWORD=123 rocker/geospatial:4.3.1

1. Open a web browser using the link below and sign in with the defined password:

bash http://localhost:8787/

1. Create a zip file of the folder:

bash ddw.framework

1. In Rstudio, upload the following file (look for the yellow-up-arrow icon in the panel to navigate through files):

bash ddw.framework.zip

1. In Rstudio, from the 'ddw.framework' folder, open the R project.

bash ddw.framework.Rproj

1. In the R console, execute:

bash renv::status()

1. Install the 'yaml' package if you get a warning message for its installation:

bash install.packages("yaml")

1. Execute and type "y"" when required to download and install the required packages (10-15 min):

bash renv::restore()

1. Execute the following function to verify that you get 'No issues found'.

bash renv::status()

1. Open the following quarto notebook file and press the "Render" icon (if required, accept the installation of packages):

bash Framework.wastewater.surveillance.qmd

1. A new browser should open, or in the files directory, look for the following html:

bash Framework.wastewater.surveillance.html

Support

This repository is expected to be in continuous improvement. For major changes, please open an issue first to discuss what you would like to improve.

License

This project is licensed under the MIT license. Feel free to edit and distribute this template as you like. See LICENSE for more information.

MIT

Acknowledgements

The authors wish to express their gratitude for the valuable support in the local implementation of this study, without which this research cannot be consolidated: Carlos Pailles, Ana Velasco, Lucía Guardamino, Rodrigo Tapia-McClung, Araceli Chávez, Diana Ramos, Daniela Goméz, José Luis Pérez, Natalia Volkow, and the anonymous facilitators from Mexico City, citizens of Tepeji del Río, and the INEGI department of microdata. Scholarship funder: CONACYT-Alianza FiiDEM.