Abstract
Trade of cattle between farms forms a complex trade network. We investigate partitions of this network for cattle trade in Germany. These partitions are groups of farms with similar properties and they are inferred directly from the trade pattern between farms. We make use of a rather new method known as stochastic block modeling (SBM) in order to divide the network into smaller units. SBM turns out to outperform the more established community detection method in the context of disease control in terms of trade restriction. Moreover, SBM is also superior to geographical based trade restrictions and could be a promising approach for disease control.
Highlights
The trade with living animals poses a major risk for the spread of infectious diseases
In order to assess the eligibility of modules and block models for animal disease control, we simulate epidemic outbreaks on the network and evaluate different control strategies based on trade restrictions according to different network partitionings
The giant strongly connected component (GSCC) of the German cattle trade network has a size of 69% of the network nodes
Summary
The trade with living animals poses a major risk for the spread of infectious diseases. Partitioning nodes according to the component structure is a useful tool for risk assessment, the component structure of livestock trade networks is typically dominated by a so-called giant component [15,16,17,18,19, 24] That is, these networks consist of continents instead of small islands. We thereby put a focus on the detection of inherent groups in the network and evaluate the feasibility of different partition methods for disease control These are community detection for finding modules, a stochastic block model, and a nested stochastic block model with hierarchical structure. In order to assess the eligibility of modules and block models for animal disease control, we simulate epidemic outbreaks on the network and evaluate different control strategies based on trade restrictions according to different network partitionings.
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