Viral disease persistence in species without a reservoir host is of importance for public health and disease management. But how can disease persistence be explained? We developed a spatially-explicit individual-based model that takes into account both ecological and viral traits as well as variable space to test disease persistence hypotheses under debate. We introduce a novel concept of modeling alternative disease courses at the individual level, causing transient infections or killing infected animals, with the lethally infected having a variable life-expectancy. We systematically distinguish between disease invasion and persistence. We use classical swine fever (CSF), an economically very important livestock disease in a social host, the wild boar, as a reference system to test and rank the persistence hypotheses under debate. Parameter values for host population demographics and CSF epidemiology reflect current knowledge. Sensitivity analysis of the model parameters revealed that the most important factor for disease persistence is a disease profile with mostly transient, i.e. surviving individuals requiring immunity, and some chronically, long-term infected animals. Immune individuals can constantly produce susceptible offspring, while some chronically infected individuals act as 'super spreaders' in time. Thus, variations in the course of the disease at the individual level are important factors determining persistence, which is usually not taken into account in the prominent measure of epidemiology, i.e. the basic reproductive number R 0 , which reflects the 'reproductive potential' of the infected sub-population. We discuss our results with regard to the general issues of modeling epidemics and disease management issues.

Summary 1.Control of animal-born diseases is a major challenge faced by applied ecologists and public health managers. To improve cost-effectiveness, the effort required to control such pathogens needs to be predicted as accurately as possible. In this context, we reviewed the anti-rabies vaccination schemes applied around the world during the past 25 years. 2. We contrasted predictions from classic approaches based on theoretical population ecology (which governs rabies control to date) with a newly developed individual-based model. Our spatially explicit approach allowed for the reproduction of pattern formation emerging from a pathogen's spread through its host population. 3. We suggest that a much lower management effort could eliminate the disease than that currently in operation. This is supported by empirical evidence from historic field data. Adapting control measures to the new prediction would save one-third of resources in future control programmes. 4. The reason for the lower prediction is the spatial structure formed by spreading infections in spatially arranged host populations. It is not the result of technical differences between models. 5. Synthesis and applications. For diseases predominantly transmitted by neighbourhood interaction, our findings suggest that the emergence of spatial structures facilitates eradication. This may have substantial implications for the cost-effectiveness of existing disease management schemes, and suggests that when planning management strategies consideration must be given to methods that reflect the spatial nature of the pathogen-host system.

Surveillance approaches for wildlife diseases often are based on strategies devised for livestock diseases. Following standard protocols, surveillance sometimes continues after apparent disease elimination. However, in the case of recurrent wildlife diseases that cause decisive morbidity and mortality, efficient and effective surveillance strategies might need to be more dynamic and adaptable to the actual epidemic situation. Here, we evaluated existing surveillance schemes by reanalyzing historic data on three wildlife diseases in Europe: rabies, classical swine fever, and avian influenza. We analyzed the aims of different surveillance activities and the way in which they were performed. Our analyses revealed that static, nonadaptive surveillance was a suboptimal approach. Consequently, we propose and discuss a more adaptive alternative scheme of situation-based surveillance for recurrent wildlife diseases that cause readily recognizable morbidity and mortality.

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