In this article, multiobjective optimization of vaccinations is studied using graph-based modelling and simulations of the spreading of the disease. Real-life dataset of animal movements between farms and pastures in the Piedmont region of Italy is used, from which a dynamic network of contacts is reconstructed. Evolutionary multiobjective optimization of vaccinations is compared with vaccination strategies based on degrees or strengths of graph nodes, number of animals in the farms as well as with the ring vaccination strategy. In the article, the influence of uncertainties represented by the lack of knowledge of initial disease cases and the change of the contacts network by a rewiring process on the vaccination optimization is studied. Results of experiments show that evolutionary optimization of vaccinations can outperform vaccination strategies when enough information is provided. When many disease cases remain unknown or when the changes in the contacts network are large, the performance of the optimization algorithm is adversely affected. Obtained results motivate further research on modelling changes in animal movement patterns, as well as hybrid methods combining evolutionary optimization with vaccination strategies.

在本文中，使用基于图的建模和疾病传播的模拟研究了疫苗接种的多目标优化。使用意大利皮埃蒙特地区农场和牧场之间动物运动的真实数据集，从中重建了动态的联系网络。根据图节点的程度或强度，农场中的动物数量以及环型疫苗接种策略，将疫苗的进化多目标优化与疫苗接种策略进行比较。在本文中，研究了由于缺乏对初始疾病病例的了解以及重新布线过程导致的接触网络变化对不确定性的影响对疫苗接种优化的影响。实验结果表明，在提供足够的信息后，疫苗接种的进化优化可以胜过疫苗接种策略。当许多疾病病例仍然未知或接触网络的变化很大时，优化算法的性能将受到不利影响。获得的结果激发了对动物运动模式变化建模的进一步研究，以及将进化优化与疫苗接种策略相结合的混合方法的进一步研究。