Sessions

In the spring of 2025, the presence of the foot-and-mouth disease virus (FMDV) was confirmed in five cattle farms in Hungary. Around the same time, outbreaks were also detected in Slovakia, involving six farms. This was the first FMDV outbreak in Hungary in more than fifty years, causing considerable concern among both scientific and economic stakeholders. Although epidemiological risk assessment cannot provide complete certainty, it can be fundamentally important for organizing future responses to the disease. Should the presence of FMDV become established, or if European decision-makers must frequently face new outbreaks, epidemiological risk analysis will play a crucial role in future epidemic management.

Our aim was to model the potential airborne spread of the virus, in the form of aerosols, using data from the Hungarian and Slovakian outbreaks, and to determine probabilistic estimates regarding the likelihood of viral transmission via this pathway. We sought to demonstrate that some infections may, with a certain probability, be attributable to the uncontrolled spread of the virus through the wind.

Our method was as follows: we examined pairs of farms, where the A-farms were considered as virus-emitting sources, while the B-farms were studied to estimate the potential virus concentration in the air surrounding the animals on a daily basis using an aerosol transmission model. The infection risk of the B-farms was then estimated by combining the modeled airborne virus concentrations circulating in the area with the number of susceptible animals present there.

For our work, we used the NOAA HYSPLIT simulation model, which is widely applied worldwide for modeling the dispersion of particles in the atmosphere. Two meteorological databases were employed for the period from February 25 to April 19, 2025: the 0.25° resolution dataset generated by the NOAA Global Forecast System (GFS) Model, and the ERA5 reanalysis dataset from ECMWF, with 31 km resolution.

The experience we have gained in our work and the modeling environment we have developed can assist in risk assessment activities supporting regulatory measures in the event of future outbreaks.