Background: Monitoring systems are essential to detect if the number of cases of a specific disease is rising. Data collected as part of voluntary disease monitoring programs is particularly useful to evaluate if control and eradication programs achieve the target. These data are characterized by random noise which makes harder to interpret temporal changes in the data. Monitoring trends in the data is a possible approach to overcome this issue. The objective of this study was to assess the performance of three time-series models that allows monitoring trends in data in terms of its adaptability when used to monitor changes in disease sero-prevalence at a national scale based on data collected as part of voluntary monitoring programs. We compared two Bayesian forecasting methods and an Exponential smoothing method, specifically a Dynamic Linear Model, a Dynamic Generalized Linear Model and a Holt's linear trend method, respectively. These three different types of time series models were applied to data on weekly sero-prevalence of Porcine Reproductive and Respiratory Syndrome (PRRS) in Danish swine herds. Results: Comparing the linear cross-dependence between the filtered values obtained from the three models and the raw data, we observed that the Holt's linear trend method shows negative linear dependence for roughly half of the time for breeding/nucleus and multiplier herds, having values close to zero for most of the period in finisher herds. Conclusions: Bayesian forecasting methods adapt faster to changes in the data, compared to the deterministic Holt's linear trend method. The practical implication of this greater flexibility is that the Bayesian methods will provide more reliable values of changes in the data and have potential to be implemented as part of a surveillance system in Denmark.