Evaluation of a hierarchical ascendant clustering process implemented in a veterinary syndromic surveillance system

Using longitudinal syndromic surveillance to describe small ruminant health in village production systems in Myanmar

A novel syndromic surveillance approach was used to describe small ruminant health in Myanmar, to help overcome limitations in disease diagnosis common in many parts of the world, especially in low and middle income countries (LMICs). Observations were made from July 2015 to June 2016 of ten clinical syndromes in 12 goat herds and sheep flocks owned by smallholders in the Central Dry Zone. Strengths and weaknesses to using syndromic surveillance in a village setting were identified using a formal surveillance evaluation framework, 'SERVAL'. Larger reporting teams made disproportionately more reports than smaller ones (86% compared to 14% of all reports, with a reporting rate ratio of 4.3 95% CI 3.5-5.4), which may have affected surveillance sensitivity. The benefits of the syndromic surveillance included its relatively low cost and ability to produce quantitative disease estimates that could be used to prioritise further disease investigation and extension activities. In particular, significant mortality was observed, with monthly mortality of 3.0% (95% CI 2.5-3.7%) and 0.28% (0.15-0.53%) in young and adult animals, respectively, and a population attributable fraction of mortality for young animals of 82% (68-91%). Mortality was associated with ill-thrift in young animals but had not previously been considered an important production-limiting condition in Myanmar. This information contributes to an understanding of the prevalence of excessive mortality in smallholder goat and sheep production systems. It is a practical example of the use of syndromic surveillance in a LMIC livestock production system, the results of which can direct future disease research, treatment and prevention to improve the health and productivity of small ruminants in Myanmar.

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

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.