Identifying the Spread Mechanism of Animal Infectious Diseases by Time-to-event Data

Music City Center 

Infectious diseases in animal farms pose a significant threat, often resulting in mass livestock mortality and substantial economic losses. The spread of diseases is driven by the interplay of multiple factors, including the recurrence of the virus within a farm, the transportation of livestock between farms, and environmental conditions. We regard the occurrence of infection events in each farm as time-to-event data on network nodes. Existing methods often assume that the interactions between nodes are static. However, the transportation between farms changes dynamically. Therefore, we propose a new version of the Hawkes Process that accounts for evolving transportation networks. Our method also allows the incorporation of time-varying environmental covariates. We further develop an Expectation-Maximization algorithm leveraging the branching structure of the model to conduct statistical inference. The algorithm also enables us to distinguish whether the infection event is externally driven or internally driven. The proposed model is validated through extensive simulations and real-world epidemic data from China.

Dynamic Network

Hawkes Process

EM algorithm

Disease Spread 

Section on Statistics in Epidemiology