Can Thinking Like an Engineer Improve Health Management?

By: Dr. Jim Lowe, Production Animal Consultation

Big data is all around us. The world of agriculture is embracing cutting-edge technologies at a rapid pace to improve agricultural practices and collect large amounts of data. It is easy to get lost in the cloud computing, new gadgets, algorithms and correlation analysis, foregoing the real mission of optimizing the technology to enhance real pig farming.

Today, tools used for disease management are complicated and highly integrated with lots of moving parts with biological, operational and human behavior variations. However, it is how we incorporate these tools all together and leverage the big data along with complex models in an appropriate manner that will move disease management forward.

The challenge at hand in modern agriculture is how to drive execution at the individual farm and also on a large scale. So, how do you do something 100 times over in a large geographic area to really make an impact?

Managing persistent diseases, such as porcine reproductive and respiratory syndrome, is frustrating for veterinarians and producers, so improved management strategies are a priority and based on the lack of past success, should cause us to rethink current practices. Creating a new path that allows us to optimize sophisticated models and big data for better disease management is about thinking differently.

Producers and animal health stakeholders can take a lesson from an engineer’s playbook and execute their threestep process — measure, model and fix.

Measure: The first step is to find the voice of the system. You have to understand what goes into the system and what comes out of the system. You have to figure out what the system is doing.

Model: Understanding how all the working parts fit together is the next step. It is important to model it mathematically or physically to understand and try potential interventions.

Fix: Finally, the last step is to integrate the solution to solve the problem. Often in animal agriculture production, we just forego the first two steps. As a practitioner, I am really good at this bucket – let’s just go fix it today. We tend to think more about fix it than how to measure and model those solutions and that leads to less than optimal solutions.

For all segments of agriculture today, one of the keys to successfully moving forward is big data. However, big data is really good at the “what” and often does not explain the “why”. Big data leaves us with simple correlations and there are pitfalls with that approach. We have to be really careful in looking at this data not to over-interpret what is happening there, particularly with causality. Applying big data is good, but not perfect.

In animal health, many significant projects are applying big data, but no one is talking or thinking about it. The challenge with not thinking about it is we are not continuously reanalyzing the process to determine if we can do it better and we are perhaps over-interpreting the data.

There are gaps in every disease management system and project. Our experience with a regional control project involving a group of producers agreeing to share data intensively is one example of how we are trying to minimize those gaps.

In most regional control projects, neighboring pig farmers agree to control disease by taking certain actions based on sharing herd health status. It is making decisions not to put PRRS-positive pigs next to another farmer’s sow farm or implementing a vaccination program regionally to drive down viral load.

The challenge to that project is, although it is very intense, it does not have enough scope to necessarily help them answer all their questions. It becomes intense shortterm execution focused and not long-term strategically focused or even long-term tactically focused on how to make the system better.

So will bigger data help solve that problem?

There are four buckets to leveraging big data: collaboration, aggregation, analysis and synthesis.

We have to think about how we aggregate the data and how we build those collaborations to allow the aggregation to happen. Then, we have to think about how we use sophisticated modern tools to do the analysis. Finally, we must decide how we synthesize the information and give it back to producers in such a way that they can drive decisions by it. The ticket to all this is we can’t move forward until we know what is going on. We have to measure the system. Thinking about this measurement idea on a macro scale is going to help us drive animal health forward over the long haul at a much faster pace.