Operator movements contribute to disease spreadOperator movements contribute to disease spread
Spread of diseases in farmed animals shown using social network analysis.
January 31, 2017
Researchers have shown that looking at movements of operators and vehicles between farms in the same way researchers look at contacts in social networks can help explain the spread of dangerous infectious diseases of livestock, such as foot and mouth disease and avian influenza.
This research, published in PLOS Computational Biology, can contribute to the development of more accurate tools for predicting the spread of livestock diseases and may help implement more effective biosecurity measures in farms.
In the study, Dr. Gianluigi Rossi and colleagues from the Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna in Italy have shown that the network of contacts originated from on-farm visits by veterinarians to dairy farms of northern Italy displays hidden features that cannot be detected by simply looking at the frequency of visits and unveils otherwise unexplained patterns of infection.
The researchers discovered that veterinarians' movements produce an unexpectedly large number of potentially infectious contacts among farms that can quickly spread dangerous livestock diseases.
The research, made possible by the availability of high-resolution data in space and time on veterinarian movements in the study area, shed light on the actual significance of operator movements in disease spread -- a still poorly understood topic due to the highly diverse and complex nature of such movements and to privacy issues in data collection.
The researchers compared the role of veterinarian movements in diseases spread with those of animal exchange between farms, which is recognized as the most effective transmission route for livestock infectious diseases. They found that co-occurrence of operator movements and animal exchanges is synergistic, largely amplifying the potential for disease propagation. The study shows how multi-layer network analysis substantially improves the way disease spread can be described, thus contributing to their control.
The article in PLOS Computational Biology is available at http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005301.
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