Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus.

Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.

Estimation of swine movement network at farm level in the US from the Census of Agriculture data.

Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

Process Review for Development of Quantitative Risk Analyses for Transboundary Animal Disease to Pathogen-Free Territories.

Outbreaks of transboundary animal diseases (TADs) have the potential to cause significant detriment to animal, human, and environmental health; severe economic implications; and national security. Challenges concerning data sharing, model development, decision support, and disease emergence science have recently been promoted. These challenges and recommendations have been recognized and advocated in the disciplines intersecting with outbreak prediction and forecast modeling regarding infectious diseases. To advance the effective application of computation and risk communication, analytical products ought to follow a collaboratively agreed common plan for implementation. Research articles should seek to inform and assist prioritization of national and international strategies in developing established criteria to identify and follow best practice standards to assess risk model attributes and performance. A well-defined framework to help eliminate gaps in policy, process, and planning knowledge areas would help alleviate the intense need for the formation of a comprehensive strategy for countering TAD outbreak risks. A quantitative assessment that accurately captures the risk of introduction of a TAD through various pathways can be a powerful tool in guiding where government, academic, and industry resources ought to be allocated, whether implementation of additional risk management solutions is merited, and where research efforts should be directed to minimize risk. This review outlines a part of a process for the development of quantitative risk analysis to collect, analyze, and communicate this knowledge. A more comprehensive and unabridged manual was also developed. The framework used in supporting the application of aligning computational tools for readiness continues our approach to apply a preparedness mindset to challenges concerning threats to global biosecurity, secure food systems, and risk-mitigated agricultural economies.