Scientists have developed models to prevent the spread of dangerous livestock infections

During an outbreak of infectious diseases in livestock, such as foot-and-mouth disease or swine flu, prompt culling of sick animals and disposal of carcasses are the best ways to stop the infection and limit its consequences. However, even if it is detected quickly, any delay can lead to the transmission of the pathogen from infected farms.

A group of researchers found that, often, developers of mathematical models of the spread of infections neglect such a factor as a delay in response. The findings may also have implications for human diseases, including covid-19 .

“Outbreaks of livestock diseases can have devastating economic, social and political consequences,” said Matthew Ferrari, assistant professor of biology. - For example, preventing outbreaks of FMD annually costs $1.5 billion worldwide in FMD-free countries, and orders of magnitude more in countries where the disease is endemic. These costs can be significantly reduced if diseased herds are quickly identified and isolated to prevent transmission to other animals.”

Using the 2001 foot-and-mouth disease outbreak in the United Kingdom as an example, Ferrari teamed up with scientist Yun Tao, a former graduate student at the University of Pennsylvania who is now a Research Fellow at the University of California, Santa Barbara, and their colleagues to create a model to generate various spread scenarios. infectious disease. First of all, the researchers were interested in how different response times would affect the overall outcome of the outbreak.

“The UK government has set ambitious targets to prevent the spread of foot-and-mouth disease, but there were still significant delays in response that exacerbated the impact,” Ferrari said.

The researchers used data from individual farms collected by the UK Department for the Environment, Food and Rural Affairs during the outbreak to examine the impact of three factors: farm size, animal infestation reporting to official authorities, and farm density.

The study authors defined farm size as the number of livestock on the farm to be culled, demand for control as the number of farms scheduled for control, and farm density as the number of infected farms within a geographic radius of 5 kilometers. They then simulated different delay times in different scenarios, including infected farms that had not yet been culled, and farms that had been culled but had carcasses remaining.

“Our results showed that farm size and control demand were key factors associated with culling and disposal activities on individual farms,” Tao said. “In particular, veterinarians have taken longer to initiate responses on larger farms, which are major sources of potential infection. They also took longer when the outbreak was in full swing, probably because the system was overloaded."

For farm size, the team's model predicted that a farm that was 100 animals larger was culled 3.7% slower and carcasses disposed of 2.2% slower. The number of farms that queued up for culling also correlated with a drop in both culling and disposal efficiency. For every 10 farms in line, the daily cull rate was reduced by 13% and the daily disposal rate by 8.4%.

The results of the study were published in The Journal of The Royal Society Interface.

“Our results show that models that assume fixed, timely responses greatly underestimate the severity of the epidemic and its long-term consequences,” says Tao. The dynamics of response and the recognition of partial controllability of interventions in our models can help prioritize management during outbreaks of livestock diseases.”

The results obtained may also have implications for modeling human infectious diseases.

“For a variety of reasons, we have seen healthcare and testing delays during the COVID-19 pandemic. Recognizing how delays in work can be exacerbated by the outbreak itself is the first step towards developing robust strategies that could reduce the number of people who fall ill during the outbreak,” said Matthew Ferrari.