Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

季节性流感可通过疫苗接种活动得到控制。流感病毒抗原的进化意味着必须更新疫苗以匹配新毒株，而疫苗的有效性取决于科学家提前近一年预测哪种流感变种将在即将到来的季节中占主导地位的能力。在这篇综述中，我们重点介绍了一种有前途的新监测工具：预测模型。这些模型基于世界卫生组织和学术科学家之间的数据共享和密切合作，利用监测数据对流感演变进行定量预测。预测模型证明了应用进化生物学在改善公共卫生和疾病控制方面的潜力。我们回顾了流感预测模型的现状，并讨论了后续步骤和建议，以确保这些模型实现其巨大的生物医学前景。