For non-regulated diseases within a livestock population, the farmer decides whether to control them or not on a voluntary basis. Nevertheless, individual decisions have an impact on the risk for other farmers to be infected. Since some farmers are grouped in associations/geographical areas, it is relevant to investigate how a group of farmers can coordinate individual decisions, implementing incentives for individual disease management. This issue is applied to a major viral disease in swine production: the porcine respiratory and reproductive syndrome (PRRS). A Markov sequential decision model is defined including stochastic compartmental models representing PRRS virus spread within a group of herds among which some are PRRS virus positive. We describe an approach to propose control strategies which are adaptive to the evolution of the epidemiological situation over time. We assumed that the collective decisionmaker should at each time-step select the incentive to optimise a criterion, for example the minimisation of the total cost at the group level (incentive, control and disease costs). The decision-maker can choose among many incentives levels, ranging from cheap no-incentive to costly incentives. We compute a policy corresponding to a guideline indicating the action to use according to the observed epidemiological situation. By simulation, we illustrate that different levels of incentive are used over time inducing an average total cost at the group level lower than if we systematically used each incentive level. While optimising the total cost, the model can be extended to consider also an objective in terms of prevalence decrease.