Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016)

Abstract

This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.

Spatio‐temporal variations in mortality during the seawater production phase of Atlantic salmon (Salmo salar) in Norway

“…In other animal production systems and in human health, systems for early detection of syndromes have recently been included in the surveillance of diseases (Dorea & Vial, ). Data used for such syndromic surveillance relate to non‐specific health indicators that enable early identification of the impact (or absence of impact) of threats to animal or human health (Triple S Project, ).…”

“…In other animal production systems and in human health, systems for early detection of syndromes have recently been included in the surveillance of diseases (Dorea & Vial, 2016 F I G U R E 7 Side-by-side confidence intervals (grey error bars) and estimated marginal mean (EMM) estimates of monthly mortality risk and purple "comparison bars" for months at sea (a), production zones (b), year of slaughter (c) and months of first stocking (d). Nonoverlapping comparison bars indicate statistical significance of pairwise differences (at the log-scale) between two levels of the variable Backer, Brouwer, Schaik, & Roermund, 2011;Morignat et al, 2014;Perrin et al, 2012).…”

Near Real-Time Monitoring of Clinical Events Detected in Swine Herds in Northeastern Spain

“…Recent initiatives have been carried out to monitor some endemic diseases in swine populations using these methods. Although many authors have pointed out the potential for near real-time monitoring, system development faces several technical, social and communication challenges in order to define data standards and data-sharing agreements (1)(2)(3)5). Implementation involves a multidisciplinary approach and the participation of the swine sector.…”

“…Getting updated information on the health status of the target swine population in near real-time can facilitate the implementation of efficient measures by swine stakeholders, veterinary practitioners, and government. Innovative surveillance methods based on the analyses of various types of data, which may serve as indirect health indicators, are under development (1)(2)(3). The ability to collect data in a cost-effective and timely manner from a wide range of sources, the use of data mining techniques and time series analyses, and the possibility of generating dynamic reproducible reports, has led to the development of new ways of conducting surveillance in near real-time (4,5).…”

Translating Big Data into Smart Data for Veterinary Epidemiology

“…The analysis of big data, as applied to veterinary epidemiology, is not fundamentally novel compared to traditional or historical practices, but rather differs in complexity, scale, and scope. Veterinary epidemiological data that are or are becoming "big" include "-omics" data, geospatial data, publically available data repositories such as World Animal Health Information System 1 and EMPRES Global Animal Disease Information System (Empres-i 2 ), clinical data or digitized health records from both companion and food animals, data on animal movement from local to international scales, and production data from food animal industries (Figure 1) (14,15,19). The analysis of such data can be used to understand health risks and minimize the impact of adverse animal health issues by, for example, increasing the effectiveness of control and surveillance by identifying high-risk populations through the analysis of spatial and animal movement data; combining disparate data or processes acting at multiple scales through epidemiological modeling approaches; and harnessing high velocity data to monitor animal health trends and for early detection of emerging health threats.…”