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

TL;DR

This paper presents an agent-based SEIRV model combined with machine learning algorithms to predict epidemic spread in wireless sensor networks, demonstrating high accuracy with certain ML methods.

Contribution

It introduces a novel integration of agent-based epidemic modeling with machine learning predictions specifically for wireless sensor networks.

Findings

Random Forest, XGBoost, Decision Trees, and k-NN achieved the best prediction accuracy.

Support vector, linear, Lasso, Ridge, and ElasticNet performed poorly.

High R^2 values indicate effective model fitting to synthetic epidemic data.

Abstract

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.