# Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models

arXiv: https://arxiv.org/abs/2511.15982 · HTML: https://mlsanity.com/paper/2511.15982/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Early Stage Malware Prediction Using Recurrent Neural Networks](https://mlsanity.com/paper/1708.03513/) (2017)
- [Evaluation of Machine Learning Algorithms for Intrusion Detection System](https://mlsanity.com/paper/1801.02330/) (2018)
- [Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks](https://mlsanity.com/paper/1708.01599/) (2017)
- [A novel agent-based simulation framework for sensing in complex adaptive environments](https://mlsanity.com/paper/1708.05875/) (2017)
- [Learning the Propagation of Worms in Wireless Sensor Networks](https://mlsanity.com/paper/2209.09984/) (2022)
- [Epidemic spreading in wireless sensor networks with node sleep scheduling](https://mlsanity.com/paper/2306.06440/) (2023)

## Abstract (arXiv)

The lack of epidemiological data in wireless sensor networks (WSNs) is a fundamental difficulty in constructing robust models to forecast and mitigate threats such as viruses and worms. Many studies have examined different epidemic models for WSNs, focusing on how malware infections spread given the network's specific properties, including energy limits and node mobility. In this study, an agent-based implementation of the susceptible-exposed-infected-recovered-vaccinated (SEIRV) mathematical model was employed for machine learning (ML) predictions. Using tools such as NetLogo's BehaviorSpace and Python, two epidemic synthetic datasets were generated and prepared for the application of several ML algorithms. Posed as a regression problem, the infected and recovered nodes were predicted, and the performance of these algorithms is compared using the error metrics of the train and test sets. The predictions performed well, with low error metrics and high R^2 values (0.997, 1.000, 0.999, 1.000), indicating an effective fit to the training set. The validation values were lower (0.992, 0.998, 0.971, and 0.999), as is typical when evaluating model performance on unseen data. Based on the recorded performances, support vector, linear, Lasso, Ridge, and ElasticNet regression were among the worst-performing algorithms, while Random Forest, XGBoost, Decision Trees, and k-nearest neighbors achieved the best results.