Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.

预防季节性流感感染的疫苗每年可挽救许多生命；但是，由于病毒的快速进化，它们必须经常更新才能保持有效。为了确定合适的疫苗株，世界卫生组织（WHO）实施了一项全球计划，该计划不断生成和解释监视数据。在过去的十年中，已经开发出了来自多个理论学科的先进的计算技术，可以预测病毒谱系占主导地位，评估其作为疫苗株的适用性，将遗传与抗原改变联系起来，以及整合和可视化遗传，流行病学，结构以及抗原性数据。这些可以利用来自监视的复杂，大规模数据类型，形成客观，可重复的疫苗菌株选择程序的基础。