Towards Practical Multimodal Hospital Outbreak Detection
This work addresses the challenge of practical and cost-effective outbreak detection in hospitals, especially for less-equipped facilities, by offering a multimodal machine learning approach that is incremental in improving surveillance methods.
The paper tackled the problem of rapid hospital outbreak detection by exploring three alternative modalities (MALDI-TOF mass spectrometry, antimicrobial resistance patterns, and electronic health records) to costly whole genome sequencing, showing that integrating these modalities can boost detection performance and proposing a tiered surveillance paradigm to reduce reliance on WGS.
Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility for routine surveillance, especially in less-equipped facilities. We explore three modalities as rapid alternatives: matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry, antimicrobial resistance (AR) patterns, and electronic health records (EHR). We present a machine learning approach that learns discriminative features from these modalities to support outbreak detection. Multi-species evaluation shows that the integration of these modalities can boost outbreak detection performance. We also propose a tiered surveillance paradigm that can reduce the need for WGS through these alternative modalities. Further analysis of EHR information identifies potentially high-risk contamination routes linked to specific clinical procedures, notably those involving invasive equipment and high-frequency workflows, providing infection prevention teams with actionable targets for proactive risk mitigation