Background: Systems for animal disease mitigation involve both surveillance activities and interventions to control the disease. They are complex organizations that are described by partial or imprecise data, making it difficult to evaluate them or make decisions to improve them. A mathematical method, called loop analysis, can be used to model qualitatively the structure and the behavior of dynamic systems; it relies on the study of the sign of the interactions between the components of the system. This method, currently widely used by ecologists, has to our knowledge never been applied in the context of animal disease mitigation systems. The objective of the study was to assess whether loop analysis could be applied to this new context. We first developed a generic model that restricted the applicability of the method to event-based surveillance systems of endemic diseases, excluding the emergence and eradication phases. Then we chose the mitigation system of highly pathogenic avian influenza (HPAI) H5N1 in Cambodia as an example of such system to study the application of loop analysis to a real disease mitigation system.
Results: Breaking down the generic model, we constructed a 6-variables model to represent the HPAI H5N1 mitigation system in Cambodia. This construction work improved our understanding of this system, highlighting the link between surveillance and control which is unclear in traditional representations of this system. Then we analyzed the effect of the perturbations to this HPAI H5N1 mitigation system that we interpreted in terms of investment in a given compartment. This study suggested that increasing intervention at a local level can optimize the system's efficiency. Indeed, this perturbation both decreases surveillance and intervention costs and reduces the disease's occurrence.
Conclusion: Loop analysis can be applied to disease mitigation systems. Its main strength is that it is easy to design, focusing on the signs of the interactions. It is a simple and flexible tool that could be used as a precursor to large-scale quantitative studies, to support reflection about disease mitigation systems structure and functioning.