Detecting Latent Transmission Pathways: A Network Anomaly Approach to Unexpected Disease Spread

Building on the heuristic of investigating deviations from expectations, and inspired by the detailed SIR and hybrid network models in Miller (2016) and Zhou et al. (2020), this research proposes a new framework for uncovering "hidden" or latent transmission routes—such as superspreader events or under-recognized contact types—by applying network anomaly detection algorithms. While most existing models focus on simulating spread via known pathways (sexual, non-sexual, vector-borne, etc.), they rarely flag or probe unexpected spikes or outlier nodes that might signal emergent or overlooked transmission mechanisms. By cross-referencing real-time incidence data with model predictions, this approach could trigger deeper investigations whenever observed transmission patterns deviate significantly from projections, potentially enabling earlier detection of novel transmission dynamics or intervention failures. The novelty lies in systematizing the search for the unexplained, offering a proactive tool for outbreak surveillance, especially in the face of evolving pathogens.

References:

1. Mathematical models of SIR disease spread with sexual-network and other transmission mechanisms. Joel C. Miller (2016).
2. Random Network Transmission and Countermeasures in Containing Global Spread of COVID-19-Alike Pandemic: A Hybrid Modelling Approach. Yimin Zhou, Jun Li, Lingjian Ye, Zuguo Chen, Qingsong Luo, Xiangdong Wu, Haiyang Ni (2020). Complex.