By Lorraine Stevenson-Hall, Biosecurity & Stakeholder Relations Lead
Many biosecurity measures are accepted as common practice across all farms, such as designating restricted areas. However, evaluating farm and site-specific risks is necessary to target biosecurity actions that will have the most impact. When prioritizing biosecurity measures, it’s important to realize that disease transmission risk increases with the frequency of an activity.
Farm specific disease risks are both internal and external. Internal risks are activities that can cause contamination within an operation, like handling of sick animals and manure management. External risks are events that could bring disease onto the farm, like feed deliveries or egg pick up.
True risk can be expressed using the probability formula: P= 1-[1-p]n where p = risk of transmission route and n = frequency of transmission route)1. Assume that the risk of disease introduction into your herd through feed delivery is 1 out of 1,000. For example, the feed truck may be carrying porcine epidemic diarrhea (PED) on its tires when it comes to the farm. If feed is delivered every week, what is the annual risk that the feed truck will introduce the pathogen? Repeated weekly, a single event with a 1/1,000 risk becomes a 1/20 risk. The event has a very small risk when it occurs only once, but becomes a much higher risk when it occurs frequently.
The frequency of the repetition of an action is very important. This is somewhat counter-intuitive because typically we focus on avoiding risks associated with unique events, like a farm tour for example. We don’t tend to worry too much about events that repeat everyday, like workers coming to the farm. While the everyday event may be low risk, repeating it frequently results in it becoming a much higher risk over time.
Burgundy Swine Farm Example
The example below shows frequency of feed truck visits to a real farm using real data. All identifying details have been changed to protect privacy. Truck 2225 visits the farm 3 times in 7 days (Aug 20, 24 and 26).
In this example, frequency causes risk of disease transmission to increase by 150 times, from a 0.1% risk from a single visit to 15% due to multiple visits.
Using the calculation and assuming three visits per week, truck 2225 poses a 15% risk of bringing a disease pathogen onto the farm per year. In this example, frequency causes risk to increase by 150 times, from a 0.1% risk from a single visit to 15% due to multiple visits. The repetition increases this risk to a level that is important to consider when prioritizing biosecurity measures.
Truck 2211 visits the farm twice on August 22
- In the same 24-hour period, this truck also visited 2 other farms and a feed mill 5 times
- Four (4) other feed trucks were also moving between the same feed mill and multiple farms that day
If Truck 2211 visits Burgundy Swine Farm twice per day each day, the annual risk that it will introduce disease to the farm is an astounding 52%! The visits to the two other farms and the feed mill increase the likelihood of disease spread between all four properties. The fact that four other feed trucks were moving between the feed mill and other farms puts multiple premises at risk.
Reduce the risk with biosecurity
Keep this principle in mind when prioritizing biosecurity measures. Knowing what events occur most frequently will help focus on reducing the risks related to the most frequent actions, rather than a singular focus on events that occur seldom or less frequently. Mitigate this risk by ensuring that trucks that come to your farm on a regular basis – like feed trucks – are washed and sanitized before entering.
Farm Health Guardian software enables users to know the exact frequency of visits to the farm, providing accurate, real-time data that will help strengthen your first line of defense against disease.
» Click here to learn more about Farm Health Guardian.
1 The formula P= 1-(1-p)n is the statistical formula to calculate cumulative proportions or probabilities based on the binomial distribution (binomial distribution derived by Jacob Bernoulli is the correct statistical distribution for 0/1 events (eg. 0 = no introduction: 1 = introduction).
