Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece

TL;DR

This study models sheep pox outbreaks in Greece from 1994-1998 using Bayesian stochastic regression, highlighting the importance of seasonality and infection counts for predicting disease incidence.

Contribution

It introduces Bayesian stochastic regression models with Ornstein-Uhlenbeck processes to analyze sheep pox epidemics, incorporating environmental and serial correlation factors.

Findings

Seasonality significantly influences sheep pox incidence.

Number of infected farms is a key predictor.

Models effectively predict epidemic trends.

Abstract

Sheep pox is a highly transmissible disease which can cause serious loss of livestock and can therefore have major economic impact. We present data from sheep pox epidemics which occurred between 1994 and 1998. The data include weekly records of infected farms as well as a number of covariates. We implement Bayesian stochastic regression models which, in addition to various explanatory variables like seasonal and environmental/meteorological factors, also contain serial correlation structure based on variants of the Ornstein-Uhlenbeck process. We take a predictive view in model selection by utilizing deviance-based measures. The results indicate that seasonality and the number of infected farms are important predictors for sheep pox incidence.