Modelling the spread of a viral infection in equine populations managed in Thoroughbred racehorse training yards.

This research developed a computer simulation to model the spread of equine influenza (EI) in racehorse training yards. The model showed various factors can influence the spread of EI, including vaccination timing and the arrival of new horses.

Research Goals and Methodology

In this research, the scientists aimed to construct a mathematical model that realistically represents the management life cycle of a population of horses in a Thoroughbred flat racing yard. To achieve this, they used the Monte Carlo technique, a computational algorithm often used in statistical physics and mathematical finance for its ability to incorporate complex probabilistic models.

• The model involved numerous state variables to represent each horse.
• Events affecting the horses were triggered stochastically, meaning randomly with probabilities given by the input assumptions, to consider both the unpredictable nature of these factors and the heterogeneity of the horse population.

Leveraging the Model for EI Spread

The newly developed model was then used to simulate the spread of equine influenza (EI) within a typical yard, assuming that one or several infectious horses were introduced to the premises.

• The goal was to produce a variety of scenarios that could provide insights into the most effective strategies for preventing EI outbreaks.
• As per the model, under typical UK management conditions and usual vaccination protocols, the spread of EI would occur.
• The timing of the vaccination in connection with the racing season and the introduction of new horses proved a critical factor in controlling the contagion.

Validation and Insights

The researchers validated the predictive outcomes of their model by comparing them with the characteristics of an existing EI vaccine.

• According to their findings, vaccination in mid-December with additional boosters in June and September emerged as a viable strategy for preventing the spread of EI in training establishments.
• The study overall provides useful computational tools for evaluating and designing EI prevention strategies tailored to the specific conditions of particular training yards.