Model predicts cross-species contamination risk for livestock
Date:
March 14, 2022
Source:
North Carolina State University
Summary:
Biosecurity efforts focused on the top 3% of farms in a particular
contact network may significantly cut back cross-species disease
dissemination.
FULL STORY ==========================================================================
A new mathematical model from researchers at North Carolina State
University reveals the high risk of cross-species disease spread on farms
with more than one type of livestock. According to the model, biosecurity efforts focused on the top 3% of farms in a particular contact network
may significantly cut back cross-species disease dissemination.
========================================================================== "Most disease-prevention programs focus control and prevention measures
on one species; however, it is well known that cross-species transmissions occur," says Gustavo Machado, assistant professor of population health and pathobiology at NC State and corresponding author of a paper describing
the work. "For example, foot-and-mouth disease can be transmitted among
all ungulate species.
And all of these farms are connected -- they sell and share animals all
the time." Machado and postdoctoral researcher Nicolas Cardenas created
a stochastic mathematical model that described the "connectedness" of
farms in one area of Southern Brazil. The model included three years'
worth of data for a population of 90 million animals and traced over
1.6 million animal movements between farms, such as animal sales and grow-finishing movements.
The model simulated disease outbreaks that began in cattle, swine,
and small ruminants (i.e., sheep or goats), respectively, in order to
determine the likelihood of cross-species contamination in each case. They
ran 1,000 distinct simulations 100 times each to identify all possible
outbreak routes.
"It doesn't matter where the outbreak starts, the entire farm -- and the
larger farm network in a community -- is at risk," Cardenas says. "We
ran simulations with diseases that are transmitted by direct contact,
and modeled outbreaks that started on both single-species and multi-host
farms to see if there was a difference in outcome, and there wasn't."
However, Cardenas says, knowing how farms interact with each other and
focusing biosecurity and prevention efforts on the most interconnected
farms does have an impact.
"The model allowed us to construct a contact network between all of
the farms in the study," Cardenas says. "The farms with the greatest
numbers of contacts, or hub farms -- regardless of how many animals
move between them -- are the focal points for disease transmission."
The researchers found that identifying the top 3% of hub farms and
focusing biosecurity efforts there dramatically reduced the number of secondarily infected farms.
"The model shows us a number of interesting points," Machado says. "First,
it shows us that we cannot look only at the immediately affected species
during an outbreak, as all of the animals are at risk. Second, if you
target biosecurity efforts toward the top ~3% of the most networked
farms you can reduce transmission on those farms and protect other
species as well.
"We hope that this model can help public health officials and
farmers target disease counteraction efforts more efficiently and cost-effectively." The work appears in Veterinary Research and was
supported by the Fundo de Desenvolvimento e Defesa Sanita'ria Animal (FUNDESA-RS) under award number 2021-1318.
========================================================================== Story Source: Materials provided by
North_Carolina_State_University. Original written by Tracey Peake. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. Nicolas C. Cardenas, Abagael L. Sykes, Francisco P. N. Lopes,
Gustavo
Machado. Multiple species animal movements: network
properties, disease dynamics and the impact of targeted
control actions. Veterinary Research, 2022; 53 (1) DOI:
10.1186/s13567-022-01031-2 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/03/220314154413.htm
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