A qualitative analysis of health information-sharing networks in the Indonesian poultry sector

Animal production systems are developing into increasingly complex value chains involving a large diversity of actors with multiple and dynamic linkages, concurrently creating many opportunities for disease spread. Access to timely and good-quality animal health information is vital for designing effective disease management strategies. However, several factors may hamper information flows along those chains. Understanding the structure and dynamics of information networks is essential to develop effective and acceptable health information systems. We applied a qualitative network approach to understand how information about poultry health is generated, disseminated and used for decision-making along the poultry value chain in Indonesia. Maps of the value chain and information networks were generated based on data from key informant interviews to illustrate the linkages and information-sharing patterns between stakeholders. Four types of farm business models were identified: company-owned, contract, partnership and independent. Although companies and most independent farmers collected health and production data routinely, their systems were strongly siloed and still relied on a mix of digital and paper-based methods, which impaired their analytical capacity. Technical service providers from the upstream sector and industry associations were identified as key intermediaries in the information-sharing network with the ability to create informal bridges between separate business networks and public actors. These actors can play a strategic role in the development of integrated information systems to improve stakeholders' capacity to monitor, anticipate and manage disease threats at all levels of the value chain. This study contributes to fill an important knowledge gap regarding the layer sector and may help decision-makers to design effective policies and interventions tailored to the type of business model.

Quantification of the sensitivity of early detection surveillance

Early detection surveillance is used for various purposes, including the early detection of non‐communicable diseases (e.g. cancer screening), of unusual increases of disease frequency (e.g. influenza or pertussis outbreaks), and the first occurrence of a disease in a previously free population. This latter purpose is particularly important due to the high consequences and cost of delayed detection of a disease moving to a new population. Quantifying the sensitivity of early detection surveillance allows important aspects of the performance of different systems, approaches and authorities to be evaluated, compared and improved. While quantitative evaluation of the sensitivity of other branches of surveillance has been available for many years, development has lagged in the area of early detection, arguably one of the most important purposes of surveillance. This paper, using mostly animal health examples, develops a simple approach to quantifying the sensitivity of early detection surveillance, in terms of population coverage, temporal coverage and detection sensitivity. This approach is extended to quantify the benefits of risk‐based approaches to early detection surveillance. Population‐based clinical surveillance (based on either farmers and their veterinarians, or patients and their local health services) provides the best combination of sensitivity, practicality and cost‐effectiveness. These systems can be significantly enhanced by removing disincentives to reporting, for instance by implementing effective strategies to improve farmer awareness and engagement with health services and addressing the challenges of well‐intentioned disease notification policies that inadvertently impose barriers to reporting.

Comparative Risk Analysis of Two Culicoides-Borne Diseases in Horses: Equine Encephalosis More Likely to Enter France than African Horse Sickness

African horse sickness (AHS) and equine encephalosis (EE) are Culicoides-borne viral diseases that could have the potential to spread across Europe if introduced, thus being potential threats for the European equine industry. Both share similar epidemiology, transmission patterns and geographical distribution. Using stochastic spatiotemporal models of virus entry, we assessed and compared the probabilities of both viruses entering France via two pathways: importation of live-infected animals or importation of infected vectors. Analyses were performed for three consecutive years (2010-2012). Seasonal and regional differences in virus entry probabilities were the same for both diseases. However, the probability of EE entry was much higher than the probability of AHS entry. Interestingly, the most likely entry route differed between AHS and EE: AHS has a higher probability to enter through an infected vector and EE has a higher probability to enter through an infectious host. Consequently, different effective protective measures were identified by 'what-if' scenarios for the two diseases. The implementation of vector protection on all animals (equine and bovine) coming from low-risk regions before their importation was the most effective in reducing the probability of AHS entry. On the other hand, the most significant reduction in the probability of EE entry was obtained by the implementation of quarantine before import for horses coming from both EU and non-EU countries. The developed models can be useful to implement risk-based surveillance.

A spatiotemporal model to assess the introduction risk of African horse sickness by import of animals and vectors in France

Background

African horse sickness (AHS) is a major, Culicoides-borne viral disease in equines whose introduction into Europe could have dramatic consequences. The disease is considered to be endemic in sub-Saharan Africa. Recent introductions of other Culicoides-borne viruses (bluetongue and Schmallenberg) into northern Europe have highlighted the risk that AHS may arrive in Europe as well. The aim of our study was to provide a spatiotemporal quantitative risk model of AHS introduction into France. The study focused on two pathways of introduction: the arrival of an infectious host (PW-host) and the arrival of an infectious Culicoides midge via the livestock trade (PW-vector). The risk of introduction was calculated by determining the probability of an infectious animal or vector entering the country and the probability of the virus then becoming established: i.e., the virus’s arrival in France resulting in at least one local equine host being infected by one local vector. This risk was assessed using data from three consecutive years (2010 to 2012) for 22 regions in France.

Results

The results of the model indicate that the annual risk of AHS being introduced to France is very low but that major spatiotemporal differences exist. For both introduction pathways, risk is higher from July to October and peaks in July. In general, regions with warmer climates are more at risk, as are colder regions with larger equine populations; however, regional variation in animal importation patterns (number and species) also play a major role in determining risk. Despite the low probability that AHSV is present in the EU, intra-EU trade of equines contributes most to the risk of AHSV introduction to France because it involves a large number of horse movements.

Conclusion

It is important to address spatiotemporal differences when assessing the risk of ASH introduction and thus also when implementing efficient surveillance efforts. The methods and results of this study may help develop surveillance techniques and other risk reduction measures that will prevent the introduction of AHS or minimize AHS’ potential impact once introduced, both in France and the rest of Europe.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-015-0435-4) contains supplementary material, which is available to authorized users.