Modeling and control of highly pathogenic avian influenza in poultry using network disease dynamics.

In light of the ongoing 2022-2025 HPAI outbreak in the U.S., which affects millions of commercial and backyard flocks, disrupts egg and meat production, and causes significant economic losses, it is important to develop biological models and optimization algorithms that conform to the U.S.-specific patterns of HPAI transmission and reflect control and prevention measures adopted in the USA. In this study, we introduce a partially stochastic network compartmental model to ascertain the progression and potential containment of HPAI virus in commercial flocks and wildlife. Parameters of the model get estimated using available data on wild bird migration, HPAI poultry outbreaks, and poultry farm inventory in different states of the U.S. The new model simulates HPAI virus transmission driven by wildlife dynamic, seasonality, and farm-to-farm relations. Unlike many prior global models, this framework is closely tailored to the U.S. HPAI statistics, farm structure, and current mitigation practices. The proposed network model, along with HPAI surveillance data, are used to analyze optimal control strategies aimed at lowering HPAI spread from wild birds to poultry. Our numerical experiments illustrate that the above control strategy is very powerful. In the absence of prevalent vaccination, these relatively inexpensive separation measures, such as covered runs and secure housing, help to prevent environmental contamination and the risk of HPAI transmission to domestic birds, thus protecting the flock and reducing depopulation.