AVTRW Irish branch 2026

Making sense of Europe’s swine respiratory disease data

Research & Conferences · October 2026

Research supported by Prowess Bioscience was presented at the AVTRW Irish Branch Annual Meeting, held on 9 October 2026 at the Atlantic Technological University, School of Veterinary Medicine in Letterkenny, Ireland.

Presented by Rose Fitzgerald, a researcher at Muster Technological University, Ireland, the work examined the epidemiology of respiratory viral pathogens in European domestic swine through a systematic review and meta-analysis. The study brought together evidence from 90 studies across 23 European countries, covering 12 pathogen categories and generating 743 quantitative result blocks for analysis

A fragmented evidence base

Respiratory viral pathogens remain a major challenge for pig health and production, but the published evidence is highly heterogeneous. Studies differ in the pathogens investigated, the populations sampled, the diagnostic methods used and the way results are reported. These differences make direct comparisons difficult and can lead to very different estimates of prevalence and seroprevalence across studies.

The review was designed to bring this fragmented evidence together in a structured way and provide a clearer picture of what is currently known across European pig populations.

What the review included

Following PRISMA 2020 guidelines, 1,497 published records underwent dual-reviewer screening. The final evidence base included:

90 studies
23 European countries
12 pathogen categories
364 extraction records
743 quantitative result blocks

Meta-analyses were conducted where sufficient independent estimates were available, including for influenza A virus, porcine reproductive and respiratory syndrome virus and porcine circovirus.

View abstract

What did the analysis show?

The results highlighted substantial variation across the published literature. For the three pathogens included in the meta-analysis, pooled herd-level prevalence was higher than individual-level prevalence:

Influenza A virus: 45.87% vs. 12.27%
PRRSV: 53.13% vs. 20.55%
Porcine circovirus: 75.19% vs. 17.30%

Patterns for seroprevalence were not uniform. Herd-level seroprevalence was higher than individual-level seroprevalence for influenza A virus, but lower for PRRSV. The most important finding, however, was the level of heterogeneity. Across the analyses, heterogeneity was high and prediction intervals were wide. This means that pooled estimates should be interpreted as averages across very different study populations and settings, rather than as universally representative values for European pig populations.

Why context matters

A pooled estimate can be useful, but on its own it can also be misleading. Differences in geography, production systems, diagnostic methods, study design and sampling strategy can all influence the prevalence estimates reported in the literature.

This is why understanding the context behind a number is just as important as the number itself. For decision-making, the value lies not only in calculating an overall estimate, but in understanding where the variability comes from, how robust the evidence is, and how confidently findings can be applied to a different population or production setting.

From evidence synthesis to better decisions

Systematic reviews and meta-analyses can help consolidate fragmented evidence, identify consistent patterns and highlight where important knowledge gaps remain. In this case, the findings provide a broad overview of respiratory viral pathogen epidemiology across Europe, while also showing the limits of generalising pooled estimates across highly diverse populations and study designs.

The results highlight priorities for future surveillance and research, including the need for more consistent methodologies, clearer reporting and careful interpretation of epidemiological estimates.

Turning complex evidence into actionable insight

At Prowess Bioscience, this type of work reflects a core part of our approach: bringing together complex and heterogeneous datasets, applying robust analytical methods and translating the results into information that can support better scientific and strategic decisions.

Whether the question involves production data, genetics, health or published evidence, the objective is the same: move beyond isolated numbers and build a clearer understanding of what the data actually mean.

View presentation

Turning complex data into actionable insights

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