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We propose a novel anomaly detection framework for COVID-19 PCR testing data by integrating Isolation Forest and t-distributed Stochastic Neighbor Embedding (t-SNE) methods. The Isolation Forest algorithm identifies anomalies by isolating points in a tree structure, while t-SNE reduces data dimensionality for visualization. The integration of these methods allows for effective anomaly detection and visualization in the high-dimensional COVID-19 PCR testing dataset. Results demonstrate the efficacy of this approach in identifying unusual patterns. We discuss the practical utility of the proposed approach in enhancing public health surveillance and decision-making.

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Isolation Forest-Based Anomaly Detection for COVID-19 PCR Testing

  • 会议论文

  • 发表日期：

    2026年

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  • Laxmi Shaw,
  • Tahir Ekin

摘要

We propose a novel anomaly detection framework for COVID-19 PCR testing data by integrating Isolation Forest and t-distributed Stochastic Neighbor Embedding (t-SNE) methods. The Isolation Forest algorithm identifies anomalies by isolating points in a tree structure, while t-SNE reduces data dimensionality for visualization. The integration of these methods allows for effective anomaly detection and visualization in the high-dimensional COVID-19 PCR testing dataset. Results demonstrate the efficacy of this approach in identifying unusual patterns. We discuss the practical utility of the proposed approach in enhancing public health surveillance and decision-making.