Code for "Identifying Complex Disease Scenarios from Cascade Data"

Contact: Abhijin Adiga (abhijin@virginia.edu)

The experiments were performed using a high-performance computing cluster in Linux environment (https://www.rc.virginia.edu/userinfo/rivanna/overview/). Therefore, the user might encounter some SLURM commands. These can be easily replaced by normal Unix shell statements.

Organization

The repository is organized in to folders. See README.md in each folder for more information. Each folder is discussed in the next section.

What is provided?

VA network

The network for VA is open to public but takes up lot of space. We have included them in a separate link: https://net.science/files/8026e893-10f6-4f03-be4b-516ea8c208a6/ TN network is not released to the public.

Computing network measures

Network measures can be computed for a small example.

• Example network and cascades. See ./examples folder.
• Code that generates cascade properties given simulation outputs and contact network. See ./simanalytics folder.

For the TN and VA networks, an HPC cluster was used.

Generating machine learning features and visualizations of network properties

Machine learning-ready tables as well as some network property visualizations can be calculated for real network measure data.

• Network measures calculated for the VA network. See ./examples/aggregated_properties.
• Code that generates ML-tables and visualizations for aggregated property files. See ./feature_extraction.

Machine learning for scenario identification

The code for all the experiments is presented. One notebook runs of the ML-tables generated by the previous module. Another notebook which contains the setup for all coverages and time cannot be run as it requires the data which is omitted due to space reasons. See ./scenario_identification.

What is not provided?

Simulator

Simulator code for the network diffusion process is not provided. This work is under review and will be made public after the review process. It has been used extensively in previous works. More information is provided in the following references.

Harrison, G., Chen, J., Mortveit, H., Hoops, S., Porebski, P., Xie, D., Wilson, M., Bhattacharya, P., Vullikanti, A., Xiong, L., Marathe, M. (2023). Synthetic Data To Support US-UK Prize Challenge For Developing Privacy Enhancing Methods: Predicting Individual Infection Risk During A Pandemic [Data set]. doi:10.18130/V3/ZOG1FF

Bhattacharya, Parantapa, et al. "Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support." The International Journal of High Performance Computing Applications 37.1 (2023): 4-27.

Bhattacharya, Parantapa, et al. "AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US." 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021.