We propose a holistic framework based on state-of-the-art methods in machine learning and optimization to prescribe influenza vaccine composition that are specific to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the flu virus: Influenza A (H1N1 and H3N2) and B viruses for production. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11–14% and mortality by 8–11% over vaccine compositions proposed by World Health Organization (WHO) for Northern Hemisphere, and lower morbidity by 8–10% and mortality by 6–9% over vaccine compositions proposed by U.S. Food and Drug Administration (FDA) for United States, and finally, lower morbidity by 10–12% and mortality by 9–11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.

中文翻译：

---

优化流感疫苗成分：机器学习方法

我们提出了一个基于机器学习和优化中最先进方法的整体框架，以根据有关主要病毒传播率的历史数据，为一个地区或一个国家开出特定于一个国家的流感疫苗组合物。首先，我们开发了一个张量完成公式，以根据历史数据预测下一季的病毒传播率。然后，考虑到主要病毒的预测循环率的不确定性，我们提出了一个新的强大的规范框架，用于为流感病毒的每个亚型选择合适的毒株：流感 A（H1N1 和 H3N2）和 B 病毒进行生产。通过数值实验，