The past two decades have seen several large scale outbreaks of epidemics. Notable among these are avian influenza, SARS, foot-and-mouth disease, citrus canker, sudden oak death and rhizomania. The structure of social and technical interactions between individuals creates an environment which makes it possible for the disease to spread not only locally, but also globally. Prevention and control of outbreaks is therefore of utmost importance in the world where diseases can travel long distances before being detected. Large scale of these outbreaks also makes them very difficult to control, as authorities are overwhelmed with the need of taking vital decisions very quickly and implementing their strategies.

Traditionally, in controlling disease outbreaks, only expenses of the infection were considered, with the aim of reducing the number of infected individuals at any cost. Nevertheless, this kind of strategy is not realistic due to rigid constrains on available resources and economic capacities. It is also important to consider prevention costs, and this leads to a concept of an optimal strategy in which we can treat only a subset of individuals. However, it is not clear how to construct an optimal set of individuals to be treated in response to observed outbreaks. On regular networks, this set usually consists of the nearest neighbors; however, for networks with non-local links or scale-free networks, it is not clear how many non-local links need to be included. Selective preventive action can result in social responses making not all potential strategies applicable.

For real systems, connections between individuals form a complicated network of interactions that can be potentially used for transmission of biological agents. Therefore, prevention and control strategies need to incorporate dynamical structure of the topology of interactions and costs associated with contact tracing. Optimal strategy can be significantly affected by additional cost incorporated by the contact recognition.

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