The role of wildlife in the epidemiology and control of Foot-and-mouth-disease And Similar Transboundary (FAST) animal diseases: A review.

Transboundary Animal Diseases (TADs) are notifiable diseases which are highly transmissible and have the potential for rapid spread regardless of national borders. Many TADs are shared between domestic animals and wildlife, with the potential to affect both livestock sector and wildlife conservation and eventually, public health in the case of zoonosis. The European Commission for the Control of Foot-and-Mouth Disease (EuFMD), a commission of the Food and Agriculture Organization of the United Nations (FAO), has grouped six TADs as 'Foot-and-mouth disease (FMD) And Similar Transboundary animal diseases' (FAST diseases). FAST diseases are ruminant infections caused by viruses, for which vaccination is a control option. The EuFMD hold-FAST strategy aims primarily at addressing the threat represented by FAST diseases for Europe. Prevention and control of FAST diseases might benefit from assessing the role of wildlife. We reviewed the role of wildlife as indicators, victims, bridge hosts or maintenance hosts for the six TADs included in the EuFMD hold-FAST strategy: FMD, peste des petits ruminants, lumpy skin disease, sheep and goatpox, Rift Valley fever and bovine ephemeral fever. We observed that wildlife can act as indicator species. In addition, they are occasionally victims of disease outbreaks, and they are often relevant for disease management as either bridge or maintenance hosts. Wildlife deserves to become a key component of future integrated surveillance and disease control strategies in an ever-changing world. It is advisable to increase our knowledge on wildlife roles in relevant TADs to improve our preparedness in case of an outbreak in previously disease-free regions, where wildlife may be significant for disease surveillance and control.

Stepping up from wildlife disease surveillance to integrated wildlife monitoring in Europe.

In a context of disease emergence and faced with the ever-growing evidence of the role of wildlife in the epidemiology of transmissible diseases, efforts have been made to develop wildlife disease surveillance (WDS) programs throughout Europe. Disease monitoring is ideally composed of "numerator data" (number of infected individuals) and "denominator data" (size of the target population). Too often however, information is available for only one. Hence, there is a need for developing integrated and harmonized disease and population monitoring tools for wildlife: integrated wildlife monitoring (IWM). IWM should have three components. Passive disease surveillance improves the likelihood of early detection of emerging diseases, while active surveillance and population monitoring are required to assess epidemiological dynamics, freedom of disease, and the outcome of interventions. Here, we review the characteristics of ongoing WDS in Europe, observe how pathogens have been ranked, and note a need for ranking host species, too. Then, we list the challenges for WDS and draw a roadmap for stepping up from WDS to IWM. There is a need to integrate and maintain an equilibrium between the three components of IWM, improve data collection and accessibility, and guarantee the adaptability of these schemes to each epidemiological context and temporal period. Methodological harmonization and centralization of information at a European level would increase efficiency of national programs and improve the follow-up of eventual interventions. The ideal IWM would integrate capacities from different stakeholder; allow to rapidly incorporate relevant new knowledge; and rely on stable capacities and funding.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

BACKGROUND: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. RESULTS: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. CONCLUSIONS: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.