Risk evaluation of nonvaccinated, weaned calves transported through areas under systematic foot and mouth disease (FMD) vaccination.

The recurrence and persistence of foot and mouth disease (FMD) could be the consequence of cyclic and massive transportation of calves. For this reason, in South America, vaccination strategies related to livestock dynamic are being promoted. In order to aid the evaluation of such strategies, a method for predicting the risk of transportation of nonvaccinated weaned calves was developed; this method combines expert opinion and empirical evidence using Bayesian estimators. It was applied through Monte Carlo simulation to data of Argentina under four hypothetical vaccination schemes: E1, extended vaccination season of 1/6 of the population of calves each month from July to December without second round vaccination (SRV); E2, extended irregular vaccination from July to December with SRV applied to 70% of the calves resembling the scheme applied in Argentina in 2001; E3, vaccination in November and December without SRV; and E4, vaccination concentrated in November. E1 resulted in probability of transporting non vaccinated calves (tnvc) reaching its maximum in the following year in May with mean=0.0250 and percentile 95% (P95)=0.0404; for the same month tnvc estimates for the other schemes were E2: mean=0.0071; P95=0.0162; E3: mean=0.0017; P95=0.0042 and E4: mean=0.0001; P95=0.0004. Bonferroni multiple comparison for simultaneous assertions for May showed that E4 resulted the best scheme, E1 the worst, and E2 and E3 are intermediate with nonsignificant difference observed between overall (p<0.05). Results were consistent with historical records and quantification for future needs for re-vaccination was made possible. While the ratio "total vaccinated"/"total estimated existences" will give a biased vision of vaccination coverage under the situation of extended vaccination campaigns, a model as the one developed here could allow a more accurate assessment and the design of mitigation plans.