Optimizing influenza vaccine composition: A machine learning approach
提出基于机器学习和优化的框架，利用历史病毒流行数据预测下一季病毒流行率，并稳健选择流感疫苗株，可降低发病率8-14%、死亡率6-11%。
Abstract 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.