Prediction of province-level outbreaks of foot-and-mouth disease in Iran using a zero-inflated negative binomial model

To identify events that could predict province-level frequency of foot-and-mouth disease (FMD) outbreaks in Iran, 5707 outbreaks reported from April 1995 to March 2002 were studied. A zero-inflated negative binomial model was used to estimate the probability of a ‘no-outbreak’ status and the number of outbreaks in a province, using the number of previous occurrences of FMD for the same or adjacent provinces and season as covariates. For each province, the probability of observing no outbreak was negatively associated with the number of outbreaks in the same province in the previous month (odds ratio [OR]=0.06, 95% confidence interval [CI]: 0.01, 0.30) and in ‘the second previous month’ (OR=0.10, 95% CI: 0.02, 0.51), the total number of outbreaks in the second previous month in adjacent provinces (OR=0.57, 95% CI: 0.36, 0.91) and the season (winter [OR=0.18, 95% CI: 0.06, 0.55] and spring [OR=0.27, 95% CI: 0.09, 0.81], compared with summer). The expected number of outbreaks in a province was positively associated with number of outbreaks in the same province in previous month (coefficient [coef]=0.74, 95% CI: 0.66, 0.82) and in the second previous month (coef=0.23, 95% CI: 0.16, 0.31), total number of outbreaks in adjacent provinces in the previous month (coef=0.32, 95% CI: 0.22, 0.41) and season (fall [coef=0.20, 95% CI: 0.07, 0.33] and spring [coef=0.18, 95% CI: 0.05, 0.31], compared to summer); however, number of outbreaks was negatively associated with the total number of outbreaks in adjacent provinces in the second previous month (coef=−0.19, 95% CI: −0.28, −0.09). The findings indicate that the probability of an outbreak (and the expected number of outbreaks if any) may be predicted based on previous province information, which could help decision-makers allocate resources more efficiently for province-level disease control measures. Further, the study illustrates use of zero inflated negative binomial model to study diseases occurrence where disease is infrequently observed.