Comparison of time-series models for monitoring temporal trends in endemic diseases sero-prevalence: lessons from porcine reproductive and respiratory syndrome in Danish swine herds

Abstract

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. Electronic supplementary material The online version of this article (10.1186/s12917-019-1981-y) contains supplementary material, which is available to authorized users.

Porcine reproductive and respiratory syndrome virus seroprevalence in Scottish finishing pigs between 2006 and 2018

“…However, comparison of results from the 2013 UK survey 8 with those from the 2012−2013 Scottish survey undertaken at a similar time indicates that the proportion of PRRSV seropositive farms was lower in Scotland. Compared with the median seroprevalence reported from 2010 to 2014 in Denmark, 32 the proportion of farms infected with PRRSV inferred from all three surveys is higher in Scotland. However, these studies used different methods which compromise a direct comparison.…”

Close to a Decade of Decrease in Antimicrobial Usage in Danish Pig Production–Evaluating the Effect of the Yellow Card Scheme

“…A univariate dynamic linear model (DLM) with a local linear trend component, as described in detail by West and Harrison (11) and applied in previous studies (12)(13)(14)(15), was used to model data at the herd level. A previous study showed that Bayesian forecasting methods adapt faster to changes in the data, compared to the deterministic Holt's linear trend methods for monitoring trends of time-series (14). The general aim of a DLM is to estimate the underlying true value of a given variable, which is expected to change over time.…”

“…where m t is the filtered mean of the antimicrobial consumption at time t and T t is the local linear trend at time t. This local linear trend was incorporated into the model to allow the system to adapt to a possible positive or negative growth in antimicrobial consumption as follows the example given in previous studies (13)(14)(15). A detailed description of the full model, as well as the R code, can be found in the literature (15).…”

Evaluating swine disease occurrence on farms using the state-space model based on meat inspection data: a time-series analysis

“…Time-series analysis using inspection data is very useful for objectively understanding the fattening process on farms. In fact, several studies have conducted time-series analyses of livestock diseases and production quantity [5,6,7]. Other studies have analyzed diseases of livestock in slaughterhouses using predictive models [8, 9,10,11].…”