Predicting Epidemic Risk from Past Temporal Contact Data

摘要: Understanding how epidemics spread in a system is crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, associated costs. This can be achieved by identifying elements at higher risk of infection implementing targeted surveillance measures. One important ingredient consider pattern disease-transmission contacts among elements, however lack data or delays providing updated records may hinder its use, especially for time-varying patterns. Here we explore what extent it possible use past temporal predict during an emerging outbreak, absence data. We focus two real-world systems; livestock displacements trade network animal holdings, sexual encounters high-end prostitution. define node’s loyalty as local measure tendency maintain same over time, uncover non-trivial correlations epidemic risk. show that assessment analysis incorporating this knowledge based structural properties provides accurate predictions both systems. Its generalizability tested introducing theoretical model generating synthetic networks. High accuracy our recovered across different settings, while amount system-specific. The proposed method provide information setup intervention strategies.