African swine fever detection and transmission estimates using homogeneous versus heterogeneous model formulation in stochastic simulations within pig premises

Abstract

Background: African swine fever (ASF) is one of the most important foreign animal diseases to the U.S. swine industry. Stakeholders in the swine  production sector are on high alert as they witness the devastation of ongoing outbreaks in some of its most important trade partner countries. Efforts to  improve preparedness for ASF outbreak management are proceeding in earnest and mathematical modeling is an integral part of these efforts.

Aim: This study aimed to assess the impact on within-herd transmission dynamics of ASF when the models used to simulate transmission assume there is homogeneous mixing of animals within a barn.

Methods: Barn-level heterogeneity was explicitly captured using a stochastic, individual pig-based, heterogeneous transmission model that considers  three types of infection transmission, (1) within-pen via nose-to-nose contact; (2) between-pen via nose-to-nose contact with pigs in adjacent pens; and (3)  both between- and within-pen via distance-independent mechanisms (e.g., via fomites). Predictions were compared between the heterogeneous and  the homogeneous Gillespie models.

Results: Results showed that the predicted mean number of infectious pigs at specific time points differed greatly between the homogeneous and  heterogeneous models for scenarios with low levels of between-pen contacts via distance-independent pathways and the differences between the two  model predictions were more pronounced for the slow contact rate scenario. The heterogeneous transmission model results also showed that it may take  significantly longer to detect ASF, particularly in large barns when transmission predominantly occurs via nose-to-nose contact between pigs in  adjacent pens.

Conclusion: The findings emphasize the need for completing preliminary explorations when working with homogeneous mixing models to ascertain  their suitability to predict disease outcomes.