Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks – Peaks, growths, and foresight in swine production

Abstract

As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

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).…”

“…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).…”

Exploring Options for Syndromic Surveillance in Aquaculture: Outbreak Detection of Salmon Pancreas Disease Using Production Data from Norwegian Farms

“…Previous research adopted autoregressive integrated moving average models [8,46] or the Holt-Winters forecasting method to deal with temporal correlation. In cases where temporal correlation could be ignored or removed, control charts such as Shewhart charts, exponentially weighted moving average charts, and cumulative sum charts were common options of aberration detection for SyS systems [5,12,[47][48][49][50][51]. However, these methods were less suitable for our system since we could not assume the constancy of the analyzed time series.…”

Managing housing and stocking density to optimize health, welfare and production in pig herds

“…Mortality can be considered as one of the most important animal health indicators, making it particularly important to record this parameter consistently Fattening pigs Bursa alterations, lameness, dirty animals, runts, and tail, flank and ear biting (Wadepohl et al, 2020) in a database (Lopes Antunes et al, 2017). Monitoring of mortality rates in combination with antibiotic usage data was found to have a predictive value in forecasting infectious disease on farms (Lopes Antunes et al, 2019). So-called 'iceberg indicators' might be helpful to assess welfare, health, management and productivity on farms to evaluate and improve housing and management systems.…”