How can we predict which pigs are most at risk of disease?

As part of the Host Pillar, Martin has been involved from the very beginning. In the project’s first phase, he worked hands-on with the extensive cohort study, where pigs were followed closely—every single day over an extended period. He contributed to sample collection in the field and to designing the study.

“This is a truly unique dataset,” he explains. “We’re following a large number of pigs in great detail over time, which gives our analyses a level of robustness you rarely see.”

Today, Martin works at the intersection of clinical understanding and data analysis. He has been involved in building the cohort stydy’s clinical database in collaboration with colleagues including Chris-tian Anton—helping ensure that the data is not only technically sound, but also meaningful from a clinical perspective.

“My role has been to look at the data through a clinician’s lens—what is important to extract, and how should it be presented to make it truly useful?”

He also serves as a bridge across the project’s different pillars. In particular, he has worked closely with the Microbiome pillar, contributing to the selection of pigs for analysis based on their clinical histories.

Identifying risk factors

A central focus of Martin’s work is identifying risk factors for diarrhea and its treatment. Using advanced analytical methods, he examines how clinical as well as microbiological factors influence a pig’s gut health before and after weaning.

“We’re looking at what increases the risk of a pig developing diarrhea or requiring treatment—and when in its life those risks are most relevant,” he says.

The implications extend well beyond research. A better understanding of risk factors opens the door to new approaches to treatment in pig production.

Today, batch treatment is still common, but Martin’s analyses point to opportunities for a more differentiated approach. By identifying high-risk pigs, treatment can be targeted more precisely—either individually or in smaller groups. 

“If you know which pigs are at higher risk of becoming unthrifty due to diarrhea, you can treat them more strategically if they become sick. Either by individual animal treatment or perhaps by grouping them by pen and providing pen treatment instead of treating the entire batch . At the same time, you can spare animals that are less likely to need treatment,” he explains.

This approach could help reduce antibiotic use while maintaining effective disease management—without placing an unreasonable burden on farmers.

A dataset with lasting potential

With around 18 months left of the project, the focus is not only on current results but also on how the data can continue to create value in the future.

For Martin, it is crucial that the dataset does not simply end as a completed research effort but remains a resource for ongoing work.
“The key is ensuring that the knowledge and data we’ve built up can continue to be used—both in re-search and in practice.”

PIG-PARADIGM thus points toward a future where decisions in the barn are increasingly informed by documented evidence—and where data becomes an active tool in improving animal health.